{"id":692,"date":"2025-08-26T23:14:13","date_gmt":"2025-08-26T23:14:13","guid":{"rendered":"https:\/\/carlaconference.org\/?page_id=692"},"modified":"2025-09-26T17:36:20","modified_gmt":"2025-09-26T17:36:20","slug":"program","status":"publish","type":"page","link":"https:\/\/carlaconference.org\/program\/","title":{"rendered":"Program"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"692\" class=\"elementor elementor-692\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aed9063 e-con-full e-flex e-con e-parent\" data-id=\"aed9063\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a35954b e-n-tabs-mobile elementor-widget elementor-widget-n-tabs\" data-id=\"a35954b\" data-element_type=\"widget\" data-settings=\"{&quot;horizontal_scroll&quot;:&quot;disable&quot;}\" data-widget_type=\"nested-tabs.default\">\n\t\t\t\t\t\t\t<div class=\"e-n-tabs\" data-widget-number=\"171283787\" aria-label=\"Tabs. Open items with Enter or Space, close with Escape and navigate using the Arrow keys.\">\n\t\t\t<div class=\"e-n-tabs-heading\" role=\"tablist\">\n\t\t\t\t\t<button id=\"e-n-tab-title-1712837871\" class=\"e-n-tab-title\" aria-selected=\"true\" data-tab-index=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"e-n-tab-content-1712837871\" style=\"--n-tabs-title-order: 1;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tMonday\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-1712837872\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-1712837872\" style=\"--n-tabs-title-order: 2;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tTuesday\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-1712837873\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"3\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-1712837873\" style=\"--n-tabs-title-order: 3;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tWednesday\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-1712837874\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"4\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-1712837874\" style=\"--n-tabs-title-order: 4;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tThursday\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-1712837875\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"5\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-1712837875\" style=\"--n-tabs-title-order: 5;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tFriday\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t\t<\/div>\n\t\t\t<div class=\"e-n-tabs-content\">\n\t\t\t\t<div id=\"e-n-tab-content-1712837871\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-1712837871\" data-tab-index=\"1\" style=\"--n-tabs-title-order: 1;\" class=\"e-active elementor-element elementor-element-4863303 e-con-full e-flex e-con e-child\" data-id=\"4863303\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-40eb4da e-flex e-con-boxed e-con e-child\" data-id=\"40eb4da\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-516448c elementor-widget elementor-widget-text-editor\" data-id=\"516448c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\">Tutorials Day | September 22, 2025<br \/><a href=\"https:\/\/carlaconference.org\/tutorials\/\">Click here for session summaries<\/a><br \/><em>Registration opens at 8:15 AM in the Grand Jamaica Suite lobby.<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-db9b010 elementor-widget elementor-widget-text-editor\" data-id=\"db9b010\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<table width=\"765\"><tbody><tr><td width=\"255\"><strong>Track 1<\/strong><br \/><em>Venue: Grand Jamaica (Montego) Suite<\/em><\/td><td width=\"255\"><strong>Track 2<\/strong><br \/><em>Venue: Grand Jamaica (Negril) Suite<\/em><\/td><td width=\"255\"><strong>Track 3<\/strong><br \/><em>Venue: Grand Jamaica (Port Antonio) Suite<\/em><\/td><\/tr><tr><td>[Empty]<\/td><td>9:00 am. <strong>A Programming Introduction to HPC<\/strong><\/td><td>9:00 am. <strong>Simulating quantum algorithms with Q-Team<\/strong><\/td><\/tr><tr><td colspan=\"3\"><em>11:00 -11:30 am. Coffee Break<\/em><\/td><\/tr><tr><td>[Empty]<\/td><td><strong>A Programming Introduction to HPC<\/strong><\/td><td><strong>Simulating quantum algorithms with Q-Team<\/strong><\/td><\/tr><tr><td colspan=\"3\"><em>1:00 &#8211; 2:00 pm. Lunch<\/em><\/td><\/tr><tr><td>[Empty]<\/td><td>2:00 pm.<strong> A Programming Introduction to HPC<\/strong><\/td><td>2:00 pm.<strong> Dist. Deep Learning: A Tutorial on Distributed Training Techniques for Large Deep Learning Models<\/strong><\/td><\/tr><tr><td colspan=\"3\"><em>4:00- 4:30 pm. Coffee Break<\/em><\/td><\/tr><tr><td>[Empty]<\/td><td><strong>A Programming Introduction to HPC<\/strong><\/td><td><strong>Dist. Deep Learning: A Tutorial on Distributed Training Techniques for Large Deep Learning Models<\/strong><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-1712837872\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-1712837872\" data-tab-index=\"2\" style=\"--n-tabs-title-order: 2;\" class=\" elementor-element elementor-element-84f250d e-con-full e-flex e-con e-child\" data-id=\"84f250d\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-6a8f37c e-flex e-con-boxed e-con e-child\" data-id=\"6a8f37c\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4acf3bb elementor-widget elementor-widget-text-editor\" data-id=\"4acf3bb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\">Workshops Day | September 23, 2025<br \/><a href=\"https:\/\/carlaconference.org\/workshops-events-2025\/\">Click here for session summaries<\/a><br \/><em>Registration opens at 8:30 AM in the Grand Jamaica Suite lobby.<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-41100aa elementor-widget elementor-widget-text-editor\" data-id=\"41100aa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<table width=\"1020\"><tbody><tr><td width=\"255\"><strong>Track 1<\/strong><br \/><em>Venue: Grand Jamaica (Montego) Suite<\/em><\/td><td width=\"255\"><strong>Track 2<\/strong><br \/><em>Venue: Grand Jamaica (Negril) Suite<\/em><\/td><td width=\"255\"><strong>Track 3<\/strong><br \/><em>Venue: Grand Jamaica (Port Antonio) Suite<\/em><\/td><td width=\"255\"><strong>Track 4<\/strong><br \/><em>Venue: Belisario Suite<\/em><\/td><\/tr><tr><td width=\"255\">9:00 am. <span style=\"text-decoration: underline;\"><a href=\"https:\/\/carlaconference.org\/lac-weather-forecasting-workshop\/\">Latin America and Caribbean Advances on Weather Forecasting<\/a><\/span><\/td><td width=\"255\">9:00 am. <span style=\"text-decoration: underline;\"><a href=\"https:\/\/ee-workshop.for.lrz.de\/\">Energy Efficiency and Sustainability in AI, HPC, and Quantum Computing<\/a><\/span><\/td><td width=\"255\">9:00 am. <a href=\"https:\/\/carlaconference.org\/hpc-centers-workshop\/\"><span style=\"text-decoration: underline;\">HPC Centers Around the World \u2013 Science and Computing Perspectives for the Latin American and Caribbean Community<\/span><\/a><\/td><td width=\"255\">9:00 am. <a href=\"https:\/\/carlaconference.org\/biocarla-workshop-challenges-and-advances-in-small-foundation-models-for-biomedical-and-life-sciences\/\"><span style=\"text-decoration: underline;\">BioCARLA 2025 \u2013 Challenges and Advances in Small\/Foundation Models for Biomedical and Life Sciences<\/span><\/a><\/td><\/tr><tr><td colspan=\"4\" width=\"1020\"><em>11:00 &#8211; 11:30 am. Coffee Break<\/em><\/td><\/tr><tr><td width=\"255\"><span style=\"text-decoration: underline;\"><a href=\"https:\/\/carlaconference.org\/lac-weather-forecasting-workshop\/\">Latin America and Caribbean Advances on Weather Forecasting<\/a><\/span><\/td><td width=\"255\"><a href=\"https:\/\/ee-workshop.for.lrz.de\/\"><span style=\"text-decoration: underline;\">Energy Efficiency and Sustainability in AI, HPC, and Quantum Computing<\/span><\/a><\/td><td width=\"255\"><a href=\"https:\/\/carlaconference.org\/hpc-centers-workshop\/\"><span style=\"text-decoration: underline;\">HPC Centers Around the World \u2013 Science and Computing Perspectives for the Latin American and Caribbean Community<\/span><\/a><\/td><td width=\"255\"><a href=\"https:\/\/carlaconference.org\/biocarla-workshop-challenges-and-advances-in-small-foundation-models-for-biomedical-and-life-sciences\/\">BioCARLA 2025 \u2013 <span style=\"text-decoration: underline;\">Challenges and Advances in Small\/Foundation Models for Biomedical and Life Sciences<\/span><\/a><\/td><\/tr><tr><td colspan=\"4\" width=\"1020\"><em>1:00 &#8211; 2:00 pm. Lunch<\/em><\/td><\/tr><tr><td width=\"255\"><a href=\"https:\/\/carlaconference.org\/lac-weather-forecasting-workshop\/\"><span style=\"text-decoration: underline;\">Latin America and Caribbean Advances on Weather Forecasting<\/span><\/a><\/td><td width=\"255\"><span style=\"text-decoration: underline;\"><a href=\"https:\/\/ee-workshop.for.lrz.de\/\">Energy Efficiency and Sustainability in AI, HPC, and Quantum Computing<\/a><\/span><\/td><td width=\"255\"><a href=\"https:\/\/carlaconference.org\/advanced-computing-trends-in-latin-america-the-caribbean-and-the-world-workshop\/\"><span style=\"text-decoration: underline;\">Advanced Computing Trends in Latin America, the Caribbean, and the World Workshop<\/span><\/a><\/td><td width=\"255\"><a href=\"https:\/\/carlaconference.org\/women-in-hpc-workshop\/\"><span style=\"text-decoration: underline;\">Women in High Performance Computing (WHPC)<\/span><\/a><\/td><\/tr><tr><td colspan=\"4\" width=\"1020\"><em>4:00 &#8211; 4:30 am. Coffee Break<\/em><\/td><\/tr><tr><td width=\"255\"><a href=\"https:\/\/carlaconference.org\/lac-weather-forecasting-workshop\/\"><span style=\"text-decoration: underline;\">Latin America and Caribbean Advances on Weather Forecasting<\/span><\/a><\/td><td width=\"255\"><span style=\"text-decoration: underline;\"><a href=\"https:\/\/ee-workshop.for.lrz.de\/\">Energy Efficiency and Sustainability in AI, HPC, and Quantum Computing<\/a><\/span><\/td><td width=\"255\"><a href=\"https:\/\/carlaconference.org\/advanced-computing-trends-in-latin-america-the-caribbean-and-the-world-workshop\/\"><span style=\"text-decoration: underline;\">Advanced Computing Trends in Latin America, the Caribbean, and the World Workshop<\/span><\/a><\/td><td width=\"255\"><a href=\"https:\/\/carlaconference.org\/women-in-hpc-workshop\/\"><span style=\"text-decoration: underline;\">Women in High Performance Computing (WHPC)<\/span><\/a><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-1712837873\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-1712837873\" data-tab-index=\"3\" style=\"--n-tabs-title-order: 3;\" class=\" elementor-element elementor-element-cd2718b e-flex e-con-boxed e-con e-child\" data-id=\"cd2718b\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-720ea9a e-flex e-con-boxed e-con e-child\" data-id=\"720ea9a\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b577504 elementor-widget elementor-widget-text-editor\" data-id=\"b577504\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\">Location: Grand Jamaica Suite<br \/><em>Registration opens at 8:30 AM in the Grand Jamaica Suite lobby.<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5f32b52 wpr-advanced-accordion-icon-side-box elementor-widget elementor-widget-wpr-advanced-accordion\" data-id=\"5f32b52\" data-element_type=\"widget\" data-settings=\"{&quot;active_item&quot;:0}\" data-widget_type=\"wpr-advanced-accordion.default\">\n\t\t\t\t\t\n            <div class=\"wpr-advanced-accordion\" data-accordion-type=\"accordion\" data-active-index=\"0\" data-accordion-trigger=\"click\" data-interaction-speed=\"0.3\">\n\n\t\t\t\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-door-open\" viewBox=\"0 0 640 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M624 448h-80V113.45C544 86.19 522.47 64 496 64H384v64h96v384h144c8.84 0 16-7.16 16-16v-32c0-8.84-7.16-16-16-16zM312.24 1.01l-192 49.74C105.99 54.44 96 67.7 96 82.92V448H16c-8.84 0-16 7.16-16 16v32c0 8.84 7.16 16 16 16h336V33.18c0-21.58-19.56-37.41-39.76-32.17zM264 288c-13.25 0-24-14.33-24-32s10.75-32 24-32 24 14.33 24 32-10.75 32-24 32z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">9:00 am | Opening Session<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>Dr. Vanesa M. Tennant Williams, Moderator<\/p><p><strong>CARLA 2025 Welcome Message<br \/><\/strong>Dr. Kevin A. Brown<br \/><em>General Co-Chair<\/em><br \/><em>CARLA 2025\u00a0<\/em><\/p><p><strong>Remarks<br \/><\/strong>Dr. Charah T. Watson<br \/><em>Executive Director<\/em><br \/><em>Scientific Research Council<\/em><strong><br \/><\/strong><\/p><p>Prof. Tannecia <span class=\"outlook-search-highlight\" data-markjs=\"true\">Stephenson<\/span><br \/><em>Deputy Dean, Faculty of Science and technology<\/em><br \/><em>Co-Director of the Climate Studies group, Mona (CSGM) <\/em><br \/><em>University of the West Indies, Mona<\/em><\/p><p>Mrs. Anika C. D. Shuttleworth<br \/><em>Chief Information Officer<\/em><br \/><em>JAMICTA - ICT Authority<\/em><\/p><p>Dr. Carlos Jaime Barrios Hernandez<br \/><em>General Chair<\/em><br \/><em>SCALAC<\/em><\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-bookmark\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M336 0H48C21.49 0 0 21.49 0 48v464l192-112 192 112V48c0-26.51-21.49-48-48-48zm0 428.43l-144-84-144 84V54a6 6 0 0 1 6-6h276c3.314 0 6 2.683 6 5.996V428.43z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">9:30 am | Invited Talk: \"Exploration Technologies to Enable NASA Missions\" by Rupak Biswas, NASA<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Abstract<\/strong>.\u00a0<span data-ogsc=\"rgb(0, 0, 0)\">NASA\u2019s missions span human and robotic space exploration, ground-breaking aeronautics research, and Earth and space sciences. This talk will provide a broad overview of numerous HPC and related technologies that NASA develops, adapts, and implements for its wide spectrum of programs and projects ranging from Earth to deep space.<\/span><\/p><p><strong>Biography.<\/strong>\u00a0Dr. Rupak Biswas is currently the Director of Exploration Technology at NASA Ames Research Center, Moffett Field, Calif., and has held this Senior Executive Service (SES) position since January 2016. In this role, he is in charge of planning, directing, and coordinating the technology development and operational activities of the organization that comprises of advanced supercomputing, human systems integration, intelligent systems, and entry systems technology. The directorate consists of approximately 700 employees with an annual budget of $160 million, and includes two of NASA\u2019s critical and consolidated infrastructures: arcjet testing facility and supercomputing facility. He is also the Manager of the High End Computing Capability Project that provides a full range of advanced computational resources and services to numerous NASA programs. In addition, he leads the emerging quantum computing effort for NASA. Dr. Biswas received his Ph.D. in Computer Science from Rensselaer in 1991, and has been at NASA ever since. During this time, he has received several agency awards, including the Exceptional Achievement Medal and the Outstanding Leadership Medal. He is an internationally recognized expert in high performance computing and has published more than 150 technical papers, received many Best Paper awards, edited several journal special issues, and given numerous lectures around the world.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-server\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M480 160H32c-17.673 0-32-14.327-32-32V64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm112 248H32c-17.673 0-32-14.327-32-32v-64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm112 248H32c-17.673 0-32-14.327-32-32v-64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">10:30 am | Diamond Sponsor Talk: What you \u201dREALLY\u201d need to know about #AI (from AI to AgenticAI) by Francisco Aguirre (Dell Technologies) & Pedro Mario Cruz e Silva  (NVIDIA)<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><span style=\"text-decoration: underline;\">What you <strong>\"REALLY\"<\/strong> need to know about <strong>#AI<\/strong> (<em>from AI to AgenticAI<\/em>)<br \/><\/span><b><i><span data-ogsc=\"rgb(21, 96, 130)\">by Francisco Aguirre<\/span><\/i><\/b> (Dell Technologies) &amp; <b><i><span data-ogsc=\"rgb(25, 107, 36)\">Pedro Mario Cruz e Silva \u00a0<\/span><\/i><\/b>(NVIDIA)<\/p><p><strong>Francisco Aguirre<\/strong>, <br \/>LATAM NVIDIA Solutions Senior Principal, Dell Technologies<\/p><p><img decoding=\"async\" class=\"wp-image-1159 alignleft\" src=\"http:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/Francisco-Aguirre-228x300.png\" alt=\"\" width=\"144\" height=\"190\" srcset=\"https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/Francisco-Aguirre-228x300.png 228w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/Francisco-Aguirre.png 748w\" sizes=\"(max-width: 144px) 100vw, 144px\" \/><\/p><p><strong>Francisco Aguirre<\/strong> is an experienced technology leader with more than 30 years in the IT industry, specializing in Artificial Intelligence, High-Performance Computing, and emerging technologies. He currently leads NVIDIA solutions for Dell Technologies in Latin America, helping organizations harness the power of accelerated computing to drive innovation and competitive advantage.<\/p><p>Throughout his career, Francisco has advised clients across key industries\u2014including finance, telecommunications, retail, airlines, and education\u2014on how to adopt and scale transformative technologies.<\/p><p>His expertise spans from data analytics, business intelligence, and big data, to modern AI deployments leveraging NVIDIA platforms, GPU-based architectures, and hybrid cloud strategies.<\/p><p>Francisco is recognized as a trusted advisor, speaker, and thought leader. He has delivered keynotes and technical sessions at major industry events such as Dell Technologies World, Dell Technologies Forum, and Mexico Business Forum.<\/p><p>His presentations focus on making cutting-edge concepts like Generative AI, Retrieval-Augmented Generation, Agentic AI, and Quantum Computing accessible to both business and technical audiences.<\/p><p>He holds a degree in Systems and Computer Science Engineering from La Salle University and a master\u2019s degree in Customer Relationship Management from Duke University. Passionate about innovation, Francisco continues to drive conversations at the intersection of technology, business, and culture.<\/p><p><strong>Pedro M\u00e1rio Cruz e Silva<\/strong>, <br \/>Senior Solutions Architect | NVIDIA Latin America<\/p><p><img decoding=\"async\" class=\"wp-image-1163 alignleft\" src=\"http:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/Pedro-Mario-Cruz-e-Silva-300x300.jpeg\" alt=\"\" width=\"187\" height=\"187\" srcset=\"https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/Pedro-Mario-Cruz-e-Silva-300x300.jpeg 300w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/Pedro-Mario-Cruz-e-Silva-150x150.jpeg 150w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/Pedro-Mario-Cruz-e-Silva.jpeg 450w\" sizes=\"(max-width: 187px) 100vw, 187px\" \/><\/p><p><strong>Pedro M\u00e1rio Cruz e Silva<\/strong> did his BSc (1995), and MSc (1998) at Federal University of Pernambuco (UFPE), he also did his DSc in 2004 at PUC-Rio. He created the Computational Geophysics Group atPUC-Rio were worked for 15 years as Manager, during this period was responsible for several Software Development and R&amp;D projects for Geophysics with strong focus on innovation. He also finished an MBA in 2015 at Get\u00falio Vargas Foundation (FGV\/RJ). Currently is Senior Solutions Architect for Higher-Education and Research for the Latin America Region.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-coffee\" viewBox=\"0 0 640 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M192 384h192c53 0 96-43 96-96h32c70.6 0 128-57.4 128-128S582.6 32 512 32H120c-13.3 0-24 10.7-24 24v232c0 53 43 96 96 96zM512 96c35.3 0 64 28.7 64 64s-28.7 64-64 64h-32V96h32zm47.7 384H48.3c-47.6 0-61-64-36-64h583.3c25 0 11.8 64-35.9 64z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">10:55 am | Coffee Break<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>Served in the<strong> Grand Jamaica Suite pre-function area<\/strong><\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-layer-group\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M12.41 148.02l232.94 105.67c6.8 3.09 14.49 3.09 21.29 0l232.94-105.67c16.55-7.51 16.55-32.52 0-40.03L266.65 2.31a25.607 25.607 0 0 0-21.29 0L12.41 107.98c-16.55 7.51-16.55 32.53 0 40.04zm487.18 88.28l-58.09-26.33-161.64 73.27c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.51 209.97l-58.1 26.33c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 276.3c16.55-7.5 16.55-32.5 0-40zm0 127.8l-57.87-26.23-161.86 73.37c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.29 337.87 12.41 364.1c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 404.1c16.55-7.5 16.55-32.5 0-40z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">11:40 am | Research Paper Session 1: Machine Learning for HPC Profiling<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>11:40 AM - 1:00 PM<br \/>Chair: <span data-olk-copy-source=\"MessageBody\">Esteban Mocskos<\/span><\/p><ol><li><em>Machine Learning for Predicting Job States and CPU Power on a Supercomputer<\/em><\/li><li><em>Performance and Energy Consumption Prediction of Scientific Workflows using Machine Learning<\/em><\/li><li><em>Profiling a task-based molecular dynamics application with a data science approach<\/em><\/li><li><em>Investigating the Impact of DVFS on the Energy Efficiency of AI Workloads on GPUs<\/em><\/li><\/ol><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">11:40 am | Paper: \"Machine Learning for Predicting Job States and CPU Power on a Supercomputer\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Dylan Benavides Castillo (Costa Rica National High Tecnology Center - CENAT); Fabricio Quir\u00f3s Corella (Costa Rica National High Tecnology Center - CENAT); Esteban Meneses (Costa Rica National High Technology Center - CENAT)<\/em><\/p><p><strong>Abstract.\u00a0<\/strong>Efficient resource management in high-performance computing (HPC) is essential for optimizing costs, reducing energy consumption, and improving system productivity. However, job variability and failures introduce uncertainties that complicate scheduling and resource allocation. Accurately predicting job failures and estimating energy consumption can enhance planning and operational efficiency. This study analyzes data from the Simple Linux Utility for Resource Management (SLURM) on the Kabr\u00e9 supercomputer at Costa Rica\u2019s National High Technology Center (CeNAT). After selecting and preprocessing relevant variables, a dataset was created to train a two-stage machine learning model comprising a binary classifier and a regression model. Using 10-fold cross-validation, multiple models were evaluated, with Random Forest emerging as the best performer in both stages. The classification model was assessed using the confusion matrix and ROC curve, while the regression model was evaluated through residual analysis and metrics such as Root Mean Square Error (RMSE) and the Coefficient of Determination (R^2). This approach can support users and administrators by improving job scheduling decisions and reducing energy waste in HPC systems.\u00a0<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">12:00 pm | Paper: \"Performance and Energy Consumption Prediction of Scientific Workflows using Machine Learning\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Felipe Barbosa (Federal University of Par\u00e1 - UFPA); Josivaldo de Souza Ara\u00fajo (Federal University of Par\u00e1 - UFPA); Marcos Amar\u00eds (Federal University of Par\u00e1 - UFPA); Erick Damasceno (Federal University of Par\u00e1 - UFPA); Fellipe Queiroz (Federal University of Par\u00e1 - UFPA); Josiany Brito Guimar\u00e3es (Federal University of Par\u00e1 - UFPA); Daniel Cordeiro (University of S\u00e3o Paulo - USP)<\/em><\/p><p><strong>Abstract.<\/strong> In High Performance Computing (HPC), large-scale scientific workflows are essential for modern discoveries but lead to significant energy consumption. This work explores predictive models to estimate both energy consumption and performance, to support sustainable computing in HPC environments. We used WfCommons to generate workflows, Wrench to simulate the supercomputing environments, and Scikit-learn to implement machine learning algorithms. Regression models, including ensemble techniques, were developed and evaluated using widely adopted scientific workflows such as BLAST, Montage, and Epigenomics. For training, features included IO time (in seconds) and the amount of bytes read and written. In energy consumption prediction, the Gradient Boosting Regressor (GBR) achieved high R^2 scores, such as 0.8556 for Epigenomics and 0.7143 for BLAST. For performance prediction, GBR also showed superior accuracy, with MAE and MAPE as low as 0.0257 and 0.0068, respectively, in the BLAST workflow. These results confirm the effectiveness of ensemble models in energy efficiency and performance, contributing to sustainable scientific computing.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">12:20 pm | Paper: \"Profiling a task-based molecular dynamics application with a data science approach\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Christian Asch (Costa Rica National High Technology Center - CENAT); Lucas Mello Schnorr (Federal University of Rio Grande do Sul - UFRGS); Esteban Meneses (Costa Rica National High Technology Center - CENAT)<\/em><\/p><p><strong>Abstract.<\/strong> Charm++ is a parallel programming framework based on task-driven execution and global object references. It has been used successfully in various high-performance computing (HPC) applications, including the molecular dynamics simulator NAMD. While Charm++ includes built-in support for performance tracing and visualization through its Projections tool, the existing system offers limited extensibility and has no support for modern data science workflows. This work presents a new visualization and analysis pipeline for Charm++ trace data that emphasizes modularity, openness, and composability. Our toolchain leverages standard scripting languages and data formats - producing output in CSV and Parquet formats - to facilitate integration with data analysis ecosystems. We demonstrate the effectiveness of this approach using LeanMD, a proxy application derived from NAMD, and highlight specific types of custom visualizations that are difficult to achieve with Projections. Our system enables custom visualizations and streamlined analysis of chare-level execution behavior, offering researchers and tool developers improved capabilities for understanding program performance and identifying load imbalance. We discuss the architecture of our tool, its application to real-world traces, and potential extensions for other task-based frameworks.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">12:40 pm | Paper: \"Investigating the Impact of DVFS on the Energy Efficiency of AI Workloads on GPUs\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Arthur Lorenzon (Federal University of Rio Grande do Sul - UFRGS); Thiago Goncalves (Federal University of Rio Grande do Sul - UFRGS)<\/em><\/p><p><strong>Abstract.<\/strong> The increasing scale of artificial intelligence (AI) models has led to unsustainable energy consumption in GPU-based systems, creating an important need for more efficient computing strategies. While Dynamic Voltage and Frequency Scaling (DVFS) can be an efficient technique, the combined impact of GPU core and memory frequencies is often not systematically evaluated. This paper presents a comprehensive analysis of how tuning both GPU core and memory frequencies affects performance, energy consumption, and the Energy-Delay Product (EDP) for AI workloads. We evaluate seven benchmarks with diverse computational demands on an AMD Radeon RX 7700XT GPU across 12 distinct frequency configurations. Our results reveal that memory-bound applications can benefit from the increase of memory frequency, reducing execution time by up to 80.7%, and compute-bound applications benefited more from higher core frequencies. In addition, by selecting appropriate operating frequencies, it is possible to reduce the EDP by up to 98.3% comparing to the worst-case scenario, highlighting the possible energy efficiency gains of choosing a balanced configuration that optimizes the energy-performance trade-off.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-utensils\" viewBox=\"0 0 416 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M207.9 15.2c.8 4.7 16.1 94.5 16.1 128.8 0 52.3-27.8 89.6-68.9 104.6L168 486.7c.7 13.7-10.2 25.3-24 25.3H80c-13.7 0-24.7-11.5-24-25.3l12.9-238.1C27.7 233.6 0 196.2 0 144 0 109.6 15.3 19.9 16.1 15.2 19.3-5.1 61.4-5.4 64 16.3v141.2c1.3 3.4 15.1 3.2 16 0 1.4-25.3 7.9-139.2 8-141.8 3.3-20.8 44.7-20.8 47.9 0 .2 2.7 6.6 116.5 8 141.8.9 3.2 14.8 3.4 16 0V16.3c2.6-21.6 44.8-21.4 48-1.1zm119.2 285.7l-15 185.1c-1.2 14 9.9 26 23.9 26h56c13.3 0 24-10.7 24-24V24c0-13.2-10.7-24-24-24-82.5 0-221.4 178.5-64.9 300.9z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">1:00 pm | Lunch<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-bookmark\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M336 0H48C21.49 0 0 21.49 0 48v464l192-112 192 112V48c0-26.51-21.49-48-48-48zm0 428.43l-144-84-144 84V54a6 6 0 0 1 6-6h276c3.314 0 6 2.683 6 5.996V428.43z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">2:00 pm | Invited Talk: \"The Limitations of Data, Machine Learning & Us\" by Ricardo Baeza-Yates, KTH Royal Institute of Technology<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p class=\"p1\"><b>Ricardo Baeza-Yates<br \/><\/b>KTH Royal Institute of Technology, Sweden<br \/>Universitat Pompeu Fabra, Barcelona<br \/>Universidad de Chile<\/p><p class=\"p1\"><b><br \/>Abstract<\/b>. Machine learning (ML), particularly deep learning, is being used everywhere. However, not always is used well, ethically and scientifically. In this talk we first do a deep dive in the limitations of supervised ML and data, its key component. We cover small data, datification, bias, predictive optimization issues, evaluating success instead of harm, and pseudoscience, among other problems. The second part is about our own limitations using ML, including different types of human incompetence: cognitive biases, unethical applications, no administrative competence, misinformation, and the impact on mental health. In the final part we discuss regulation on the use of AI and responsible AI principles, that can mitigate the problems outlined above.<\/p><p class=\"p1\"><b>Biography<\/b>. Ricardo Baeza-Yates is a a part-time WASP Professor at KTH Royal Institute of Technology in Stockholm, as well as part-time professor at the departments of Engineering of Universitat Pompeu Fabra in Barcelona and Computer Science of University of Chile in Santiago. Before, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from 2006 to 2016. He is co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 1999 and 2011 (2nd ed), that won the ASIST 2012 Book of the Year award. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow. He has won national scientific awards in Chile (2024) and Spain (2018), among other accolades and distinctions. He obtained a Ph.D. in CS from the University of Waterloo, Canada, and his areas of expertise are responsible AI, web search and data mining plus data science and algorithms in general.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-bookmark\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M336 0H48C21.49 0 0 21.49 0 48v464l192-112 192 112V48c0-26.51-21.49-48-48-48zm0 428.43l-144-84-144 84V54a6 6 0 0 1 6-6h276c3.314 0 6 2.683 6 5.996V428.43z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">3:00 pm | Panel: \u201cHPC Everyday and Everywhere\u201d<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>Moderator: Addison Snell (Intersect360)<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-coffee\" viewBox=\"0 0 640 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M192 384h192c53 0 96-43 96-96h32c70.6 0 128-57.4 128-128S582.6 32 512 32H120c-13.3 0-24 10.7-24 24v232c0 53 43 96 96 96zM512 96c35.3 0 64 28.7 64 64s-28.7 64-64 64h-32V96h32zm47.7 384H48.3c-47.6 0-61-64-36-64h583.3c25 0 11.8 64-35.9 64z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">4:00 pm | Coffee Break<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-layer-group\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M12.41 148.02l232.94 105.67c6.8 3.09 14.49 3.09 21.29 0l232.94-105.67c16.55-7.51 16.55-32.52 0-40.03L266.65 2.31a25.607 25.607 0 0 0-21.29 0L12.41 107.98c-16.55 7.51-16.55 32.53 0 40.04zm487.18 88.28l-58.09-26.33-161.64 73.27c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.51 209.97l-58.1 26.33c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 276.3c16.55-7.5 16.55-32.5 0-40zm0 127.8l-57.87-26.23-161.86 73.37c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.29 337.87 12.41 364.1c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 404.1c16.55-7.5 16.55-32.5 0-40z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">4:35 pm | Research Paper Session 2: Energy Efficiency and Scheduling<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>4:35 PM - 5:55 PM<br \/>Chair: <span data-olk-copy-source=\"MessageBody\">Kyle Felker, Tadeu Gomes<\/span><\/p><ol><li><em>Evaluating Malleable Job Scheduling in HPC Clusters using Real-World Workloads<\/em><\/li><li><em>Optimizing the Energy-Efficiency of QMCPACK on Aurora Supercomputer via GPU Sharing<\/em><\/li><li><em>Good Sustainability Practices for Data Center: A Systematic Literature Review<\/em><\/li><li>Parallel\/distributed computing for optimizing investment planning in electricity markets<\/li><\/ol><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">4:35 pm | Paper: \"Evaluating Malleable Job Scheduling in HPC Clusters using Real-World Workloads\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Patrick Zojer (University of Kassel); Jonas Posner (University of Kassel); Taylan \u00d6zden (Technical University of Darmstadt)<\/em><\/p><p><strong>Abstract.<\/strong> Optimizing resource utilization in high-performance computing (HPC) clusters is essential for maximizing both system efficiency and user satisfaction. However, traditional rigid job scheduling often results in underutilized resources and increased job waiting times. This work evaluates the benefits of resource elasticity, where the job scheduler dynamically adjusts the resource allocation of malleable jobs at runtime. Using real workload traces from the Cori, Eagle, and Theta supercomputers, we simulate varying proportions (0\u2013100%) of malleable jobs with the ElastiSim software. We evaluate five job scheduling strategies, including a novel one that maintains malleable jobs at their preferred resource allocation when possible. Results show that, compared to fully rigid workloads, malleable jobs yield significant improvements across all key metrics. Considering the best-performing scheduling strategy for each supercomputer, job turnaround times decrease by 37\u201367%, job makespan by 16\u201365%, job wait times by 73\u201399%, and node utilization improves by 5\u201352%. Although improvements vary, gains remain substantial even at 20% malleable jobs. This work highlights important correlations between workload characteristics (e.g., job runtimes and node requirements), malleability proportions, and scheduling strategies. These findings confirm the potential of malleability to address inefficiencies in current HPC practices and demonstrate that even limited adoption can provide substantial advantages, encouraging its integration into HPC resource management.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">4:55 pm | Paper: \"Optimizing the Energy-Efficiency of QMCPACK on Aurora Supercomputer via GPU Sharing\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong>\u00a0<em>Matheus Costa (Federal University of Rio Grande do Sul - UFRGS); Philippe Navaux (Federal University of Rio Grande do Sul - UFRGS); Silvio Rizzi (Argonne National Laboratory); Arthur Lorenzon (Federal University of Rio Grande do Sul - UFRGS)<\/em><\/p><p><strong>Abstract.<\/strong> As high-performance computing advances toward the exascale, energy efficiency has become a primary concern. However, complex scientific applications often underutilize GPU hardware, such as GPUs, leaving valuable resources idle. In this scenario, this paper investigates GPU sharing as a strategy to improve resource utilization and energy efficiency for the QMCPACK application on the Aurora supercomputer. We evaluate Intel's Multiple Compute Command Streamers (CCS) technology across a scale of 1 to 16 nodes on Aurora, comparing a baseline single-rank-per-GPU-tile configuration (1-CCS) against multi-rank setups with two (2-CCS) and four (4-CCS) ranks per tile. Our results demonstrate that GPU sharing via the 2-CCS mode yields significant performance improvements regarding the Figure of Merit (FOM) by an average of 17% and enhances energy efficiency (FOMe) by 32% compared to the baseline configuration that uses 1-CCS only. We also show that the 4-CCS configuration suffers from increased MPI communication overhead and lower vectorization efficiency, being worse for performance and energy than running with only 1-CCS. On the other hand, 2-CCS achieves a better balance between higher compute unit engagement and reduced memory system stalls. We also show that exploiting GPU sharing can be an effective strategy for boosting both throughput and sustainability on modern HPC systems, but over-partitioning can introduce overheads that cancel out the benefits.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">5:15 pm | Paper: \"Good Sustainability Practices for Data Center: A Systematic Literature Review\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Josiany Brito Guimar\u00e3es (Federal University of Par\u00e1 - UFPA); Felipe Barbosa (Federal University of Par\u00e1 - UFPA); Erick Damasceno (Federal University of Par\u00e1 - UFPA); Fellipe Queiroz (Federal University of Par\u00e1 - UFPA); Marcos Amar\u00eds (Federal University of Par\u00e1 - UFPA)<\/em><\/p><p><strong>Abstract.<\/strong> The search for sustainable practices and energy efficiency in data centers has become a top priority in today\u2019s world, driven by the rapid growth in internet use and online traffic. This highlights the urgent need for research that focuses on practical strategies to reduce the environmental impact of these vital facilities. This article reviews 10 relevant studies that examine the primary methods used to enhance energy efficiency in data centers, including emerging technologies, resource management techniques, and sustainable policies. Additionally, the paper discusses the environmental impacts of data centers, including high energy consumption, greenhouse gas emissions, and intensive use of natural resources. The findings indicate that implementing good sustainability practices can bring several benefits, such as lowering operating costs and reducing the carbon footprint, while maintaining service quality.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">5:35 pm | Paper: \"Parallel\/distributed computing for optimizing investment planning in electricity markets\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Santiago Freire (Universidad de la Rep\u00fablica); Sergio Nesmachnow (Universidad de la Rep\u00fablica); Pedro Moreno (Universidad Autonoma del Estado de Morelos - UAEM)<\/em><\/p><p><strong>Abstract.<\/strong> This article presents a parallel and distributed computing strategy for optimizing investment planning in electricity markets. An implementation is developed for a stochastic dynamic programming model used by the Uruguayan electrical company to determine investment strategies in generation assets. The sequential execution of thousands of solver instances results in high computing times, rendering the process inefficient for large-scale scenarios. A distributed architecture based on Message Passing Interface is proposed to enable parallel execution across multiple computing resources. The solution includes modular components organized in a layered architecture, load balancing and fault recovery features. Performance results indicate that the parallel implementation allows addressing complex scenarios with high computing demands. Significant reductions in execution time were obtained, with high speedup values (up to 98x) and efficiency up to 0.86 for a full-scale scenario executed on distributed nodes of a high-performance computing cluster.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                            <\/div>\n        \t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-1712837874\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-1712837874\" data-tab-index=\"4\" style=\"--n-tabs-title-order: 4;\" class=\" elementor-element elementor-element-0ec9d38 e-flex e-con-boxed e-con e-child\" data-id=\"0ec9d38\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-73076ba e-flex e-con-boxed e-con e-child\" data-id=\"73076ba\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3124c1a elementor-widget elementor-widget-text-editor\" data-id=\"3124c1a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\">Location: Talk of the Town<br \/><em>Registration opens at 8:30 AM in the Talk of the Town lobby.<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2396efa wpr-advanced-accordion-icon-side-box elementor-widget elementor-widget-wpr-advanced-accordion\" data-id=\"2396efa\" data-element_type=\"widget\" data-settings=\"{&quot;active_item&quot;:0}\" data-widget_type=\"wpr-advanced-accordion.default\">\n\t\t\t\t\t\n            <div class=\"wpr-advanced-accordion\" data-accordion-type=\"accordion\" data-active-index=\"0\" data-accordion-trigger=\"click\" data-interaction-speed=\"0.2\">\n\n\t\t\t\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-door-open\" viewBox=\"0 0 640 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M624 448h-80V113.45C544 86.19 522.47 64 496 64H384v64h96v384h144c8.84 0 16-7.16 16-16v-32c0-8.84-7.16-16-16-16zM312.24 1.01l-192 49.74C105.99 54.44 96 67.7 96 82.92V448H16c-8.84 0-16 7.16-16 16v32c0 8.84 7.16 16 16 16h336V33.18c0-21.58-19.56-37.41-39.76-32.17zM264 288c-13.25 0-24-14.33-24-32s10.75-32 24-32 24 14.33 24 32-10.75 32-24 32z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">9:00 am | Welcome<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-bookmark\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M336 0H48C21.49 0 0 21.49 0 48v464l192-112 192 112V48c0-26.51-21.49-48-48-48zm0 428.43l-144-84-144 84V54a6 6 0 0 1 6-6h276c3.314 0 6 2.683 6 5.996V428.43z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">9:10 am | Keynote: \"Infrastructure for New Ideas\" by Kate Keahey, Argonne National Lab & University of Chicago<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Abstract<\/strong>. We live in interesting times where new ideas in computing and technology emerge at ever increasing rates in AI, edge computing and IoT, and programmable networking to name just a few. These innovations open up unprecedented opportunities for all kinds of scientific progress from biotechnology to engineering. Given their critical role, the question arises who creates opportunities for computer science? How can we create a scientific instrument where computer science ideas can be tried, tested, and adopted or discarded? What would such a scientific instrument look like and would it engage with its community? How would such instrument evolve to follow the evolution of science? And lastly, how would it negotiate transition from innovation to mainstream adoption?<\/p><p>In this talk, I will talk about how to build this type of scientific instrument supporting the exploration of new ideas in the cyberinfrastructure space. I will share the insights, design strategy, and the lessons learned from building and operating the Chameleon computer science research platform through the last decade. We will take the journey from a base cloud testbed design and track its evolution through diversifying its hardware to support innovative architectures (e.g., Fugaku nodes), accelerators, disaggregated hardware (Liqid, GigaIO), and ultimately taking it form the datacenter and into the field by introducing support for edge hardware based on single board computers (Raspberry Pis and NVIDIA nanons). We will see how the platform evolved to support emergent ideas coming from its by now 13,000 strong user community at a reasonable cost, and allowing it to support a significant scientific output of 800+ publications. Lastly, I will share stories of research and education projects in the edge to cloud continuum and discuss their impact both on science and on scientific sharing through reproducible digital artifacts.<\/p><p><strong>Biography<\/strong>. Kate Keahey is one of the pioneers of infrastructure cloud computing. She created the\u00a0<a title=\"http:\/\/www.nimbusproject.org\/\" href=\"http:\/\/www.nimbusproject.org\/\" data-ogsc=\"\" data-outlook-id=\"a4ac422d-dcba-4ef0-9f33-efbceb98fa1c\">Nimbus project<\/a>, recognized as the first open source Infrastructure-as-a-Service implementation, and continues to work on research aligning cloud computing concepts with the needs of scientific datacenters and applications. To facilitate such research for the community at large, Kate leads the\u00a0<a title=\"http:\/\/www.chameleoncloud.org\/\" href=\"http:\/\/www.chameleoncloud.org\/\" data-ogsc=\"\" data-outlook-id=\"f23c8ce7-5d38-4dcb-9c99-9a7e29ef06d9\">Chameleon project<\/a>, providing a deeply reconfigurable, large-scale, and open experimental platform for Computer Science research. To foster the recognition of contributions to science made by software projects, Kate co-founded the <a title=\"http:\/\/www.journals.elsevier.com\/softwarex\/\" href=\"http:\/\/www.journals.elsevier.com\/softwarex\/\" data-ogsc=\"\" data-outlook-id=\"0615464c-4066-46f6-a47d-adb5e90d554d\">SoftwareX<\/a> journal, a new format designed to publish software contributions. Kate is a Scientist at Argonne National Laboratory and a Senior Scientist The University of Chicago Consortium for Advanced Science and Engineering (UChicago CASE).<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-server\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M480 160H32c-17.673 0-32-14.327-32-32V64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm112 248H32c-17.673 0-32-14.327-32-32v-64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm112 248H32c-17.673 0-32-14.327-32-32v-64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">10:10 am | Diamond Sponsor Talk: \"AI for Science and Engineering\" by Ulysses Darly Galasso (Lenovo) and Pedro Mario Cruz e Silva (Nvidia)<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>AI for Science and Engineering\u00a0 (Lenovo - Nvidia)<\/strong><\/p><p><img decoding=\"async\" class=\"alignnone wp-image-1193\" src=\"http:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/headshot-300x300.jpg\" alt=\"\" width=\"189\" height=\"189\" srcset=\"https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/headshot-300x300.jpg 300w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/headshot-150x150.jpg 150w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/headshot.jpg 352w\" sizes=\"(max-width: 189px) 100vw, 189px\" \/><br \/>Ulysses Darly Galasso<br \/>Sales Manager, HPC | <span data-markjs=\"true\">Lenovo<\/span> ISG LA<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-1194\" src=\"http:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/pedro-300x300.jpeg\" alt=\"\" width=\"190\" height=\"190\" srcset=\"https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/pedro-300x300.jpeg 300w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/pedro-150x150.jpeg 150w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/pedro.jpeg 450w\" sizes=\"(max-width: 190px) 100vw, 190px\" \/><br \/>Pedro Mario Cruz e Silva <a href=\"mailto:pcruzesilva@nvidia.com\">pcruzesilva@nvidia.com<\/a><br \/>Senior Solutions Architect | NVIDIA Latin America<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-server\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M480 160H32c-17.673 0-32-14.327-32-32V64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm112 248H32c-17.673 0-32-14.327-32-32v-64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm112 248H32c-17.673 0-32-14.327-32-32v-64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">10:22.5 am | Diamond Sponsor Talk: \"Accelerating HPC and AI with Lenovo Servers Powered by Intel\u00ae Xeon\u00ae 6\" by Tarcisio Alves (Intel)<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Accelerating HPC and AI with Lenovo Servers Powered by Intel\u00ae Xeon\u00ae 6<\/strong><strong>\u00a0 (Intel - Lenovo)<\/strong><\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-1280\" src=\"http:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/TarcisioAlvesPhoto-300x300.jpg\" alt=\"\" width=\"191\" height=\"191\" srcset=\"https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/TarcisioAlvesPhoto-300x300.jpg 300w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/TarcisioAlvesPhoto-150x150.jpg 150w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/TarcisioAlvesPhoto-768x768.jpg 768w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/TarcisioAlvesPhoto-600x600.jpg 600w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/TarcisioAlvesPhoto.jpg 800w\" sizes=\"(max-width: 191px) 100vw, 191px\" \/><\/p><p>Tarcisio Alves<strong><br \/><\/strong>Industry Technology Sales Specialist DC\/AI | Intel Brazil.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-server\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M480 160H32c-17.673 0-32-14.327-32-32V64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm112 248H32c-17.673 0-32-14.327-32-32v-64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm112 248H32c-17.673 0-32-14.327-32-32v-64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">10:35 am | Gold Sponsor Talks: \"HPC Solutions for Top Performance and lowest Energy Consumption for Research Centers\" by Miguel Tiempos (AMD)<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Abstract<\/strong>. Utilization of AMD's Epyc CPU's for Parallel Processing in HPC Environments For Super Compute applications, and Cutting Edge Artificial Intelligence Analytics<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-1182 alignleft\" src=\"http:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/Miguel-Tiempos.jpg-269x300.jpeg\" alt=\"\" width=\"172\" height=\"192\" srcset=\"https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/Miguel-Tiempos.jpg-269x300.jpeg 269w, https:\/\/carlaconference.org\/wp-content\/uploads\/2025\/09\/Miguel-Tiempos.jpg.jpeg 520w\" sizes=\"(max-width: 172px) 100vw, 172px\" \/><\/p><p><strong>Biography<\/strong>. Miguel Tiempos, <br \/>Long track in HPC coming from Cray Super Computers and later Acquisition by HPE.<br \/>Has worked on many deployments of High End Supercomputer systems for all types of workloads for Research and Higher Education Organizations as Well as National Security Entities across Latin America.<br \/>Deep knowledge in the Scheduling of HPC Workloads, Hybrid ecosystems for ML Trainings, container based deployments, and automation of scalable infrastructure solutions for HPC and AI.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-server\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M480 160H32c-17.673 0-32-14.327-32-32V64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm112 248H32c-17.673 0-32-14.327-32-32v-64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm112 248H32c-17.673 0-32-14.327-32-32v-64c0-17.673 14.327-32 32-32h448c17.673 0 32 14.327 32 32v64c0 17.673-14.327 32-32 32zm-48-88c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24zm-64 0c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">10:45 am | Sponsor Talk: \"Bridging High Performance Computing and AI\/LLM Users: An Integrated Framework Architecture\" by Lincoln V. Walters (LVW Electronics Systems Inc.)<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><em>Enabling Accessible, Secure, and Scalable AI on HPC Infrastructure<\/em><\/p><p><strong>Abstract<\/strong>. The convergence of High-Performance Computing (HPC) and Artificial Intelligence (AI), particularly large language models (LLMs), is reshaping the computational landscape for both scientific and enterprise domains. However, significant technical and usability barriers hinder the seamless integration of HPC resources with AI\/LLM workflows. This paper presents a comprehensive, modular framework architecture that bridges these domains, addressing core challenges in usability, automation, security, and extensibility. The proposed framework integrates portal\/API layers, orchestration engines, data fabrics, containerization, workflow automation, monitoring\/reporting, and robust security. We detail its design principles, highlight its modularity and support for hybrid deployments and federated learning, and analyze real-world use cases in academic research, enterprise AI, and scientific simulation. Our evaluation demonstrates enhanced accessibility, scalability, and reproducibility, positioning the framework as a foundation for future AI-HPC integration.<\/p><p><strong>Biography<\/strong>. Lincoln V. Walters, CEO, LVW ELECTRONICS SYSTEMS INC.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-coffee\" viewBox=\"0 0 640 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M192 384h192c53 0 96-43 96-96h32c70.6 0 128-57.4 128-128S582.6 32 512 32H120c-13.3 0-24 10.7-24 24v232c0 53 43 96 96 96zM512 96c35.3 0 64 28.7 64 64s-28.7 64-64 64h-32V96h32zm47.7 384H48.3c-47.6 0-61-64-36-64h583.3c25 0 11.8 64-35.9 64z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">10:55 am | Coffee Break<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>Served in the <strong>Talk of the Town<\/strong><\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-coffee\" viewBox=\"0 0 640 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M192 384h192c53 0 96-43 96-96h32c70.6 0 128-57.4 128-128S582.6 32 512 32H120c-13.3 0-24 10.7-24 24v232c0 53 43 96 96 96zM512 96c35.3 0 64 28.7 64 64s-28.7 64-64 64h-32V96h32zm47.7 384H48.3c-47.6 0-61-64-36-64h583.3c25 0 11.8 64-35.9 64z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">11:35 am | Panel: \"European Union \u2013 Latin America & Caribbean Collaborations\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>TBA<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-layer-group\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M12.41 148.02l232.94 105.67c6.8 3.09 14.49 3.09 21.29 0l232.94-105.67c16.55-7.51 16.55-32.52 0-40.03L266.65 2.31a25.607 25.607 0 0 0-21.29 0L12.41 107.98c-16.55 7.51-16.55 32.53 0 40.04zm487.18 88.28l-58.09-26.33-161.64 73.27c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.51 209.97l-58.1 26.33c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 276.3c16.55-7.5 16.55-32.5 0-40zm0 127.8l-57.87-26.23-161.86 73.37c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.29 337.87 12.41 364.1c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 404.1c16.55-7.5 16.55-32.5 0-40z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">12:35 pm | Research Paper Session 3: Parallel and Distributed Computing in Support of Applications<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>12:35 PM - 1:15 PM<br \/>Chair: <span data-olk-copy-source=\"MessageBody\">Harold Castro<\/span><\/p><ol><li>Driving Computational Efficiency in Large-Scale Platforms using HPC Technologies<\/li><li>A Scalable and Reproducible Parsl Framework for Molecular evolutionary Analyses on HPC Systems<\/li><\/ol><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">12:35 pm | Paper: \"Driving Computational Efficiency in Large-Scale Platforms using HPC Technologies\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Alexander Mart\u00ednez M\u00e9ndez (Universidad Industrial de Santander - UIS); Antonio Rubio Montero (Centre for Energy, Environmental and Technological Research - CIEMAT); Carlos Jaime Barrios Hernandez (Universidad Industrial de Santander - UIS); Hern\u00e1n Asorey (piensas.xyz); Rafael Mayo-Garc\u00eda (Centre for Energy, Environmental and Technological Research - CIEMAT); Luis Alberto Nu\u00f1ez Villavicencio (Universidad Industrial de Santander - UIS)<\/em><\/p><p><strong>Abstract.<\/strong> The Latin American Giant Observatory (LAGO) project utilizes extensive High-Performance Computing (HPC) resources for complex astroparticle physics simulations, making resource efficiency critical for scientific productivity and sustainability. This article presents a detailed analysis focused on quantifying and improving HPC resource utilization efficiency specifically within the LAGO computational environment. The core objective is to understand how LAGO's distinct computational workloads\u2014characterized by a prevalent coarse-grained, task-parallel execution model\u2014consume resources in practice. To achieve this, we analyze historical job accounting data from the EGI FedCloud platform, identifying primary workload categories (Monte Carlo simulations, data processing, user analysis\/testing) and evaluating their performance using key efficiency metrics (CPU utilization, walltime utilization, and I\/O patterns). Our analysis reveals significant patterns, including high CPU efficiency within individual simulation tasks contrasted with the distorting impact of short test jobs on aggregate metrics. This work pinpoints specific inefficiencies and provides data-driven insights into LAGO's HPC usage. The findings directly inform recommendations for optimizing resource requests, refining workflow management strategies, and guiding future efforts to enhance computational throughput, ultimately maximizing the scientific return from LAGO's HPC investments.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">12:55 pm | Paper: \"A Scalable and Reproducible Parsl Framework for Molecular evolutionary Analyses on HPC Systems\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Rafael Terra (National Laboratory for Scientific Computing - LNCC); Hugo Oliveira (National Laboratory for Scientific Computing - LNCC); Daniel Janies (University of North Carolina at Charlotte); Hiago Rocha (National Laboratory for Scientific Computing - LNCC); Diego Carvalho (Federal Center for Technological Education Celso Suckow da Fonseca - CEFET\/RJ); Carla Osthoff (National Laboratory for Scientific Computing - LNCC); Kary Oca\u00f1a (National Laboratory for Scientific Computing - LNCC)<\/em><\/p><p><strong>Abstract.<\/strong> Codon-based model testing is fundamental to molecular evolution studies. The growing complexity of these analyses -- driven by computationally intensive likelihood estimations, memory-demanding datasets, and the need to scale across hundreds or thousands of genes -- necessitates efficient use of high-performance computing resources. To address this need, we present HighSPA, a scalable and reproducible framework that integrates two widely used tools for evolutionary analysis -- CodeML and HyPhy -- into parallel workflows using the Parsl library. We validated HighSPA using DENV genomes from Brazil (serotypes 1\u20134), applying six codon substitution models. When using the CodeML workflow, HighSPA identified high-confidence positively selected sites (PSS) with serotype-specific patterns. In contrast, the HyPhy workflow detected fewer PSS, likely due to its more conservative inference approach. In terms of performance, HighSPA-HyPhy significantly reduced makespan and increased throughput -- by an average of 87x and 89x, respectively -- compared to the sequential execution of the analyses. These results support the presence of adaptive evolution in key genes such as E, NS3, and NS5, and demonstrate HighSPA\u2019s effectiveness for large-scale evolutionary analysis.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-utensils\" viewBox=\"0 0 416 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M207.9 15.2c.8 4.7 16.1 94.5 16.1 128.8 0 52.3-27.8 89.6-68.9 104.6L168 486.7c.7 13.7-10.2 25.3-24 25.3H80c-13.7 0-24.7-11.5-24-25.3l12.9-238.1C27.7 233.6 0 196.2 0 144 0 109.6 15.3 19.9 16.1 15.2 19.3-5.1 61.4-5.4 64 16.3v141.2c1.3 3.4 15.1 3.2 16 0 1.4-25.3 7.9-139.2 8-141.8 3.3-20.8 44.7-20.8 47.9 0 .2 2.7 6.6 116.5 8 141.8.9 3.2 14.8 3.4 16 0V16.3c2.6-21.6 44.8-21.4 48-1.1zm119.2 285.7l-15 185.1c-1.2 14 9.9 26 23.9 26h56c13.3 0 24-10.7 24-24V24c0-13.2-10.7-24-24-24-82.5 0-221.4 178.5-64.9 300.9z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">1:15 pm | Lunch<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-bookmark\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M336 0H48C21.49 0 0 21.49 0 48v464l192-112 192 112V48c0-26.51-21.49-48-48-48zm0 428.43l-144-84-144 84V54a6 6 0 0 1 6-6h276c3.314 0 6 2.683 6 5.996V428.43z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">2:30 pm | Keynote: \"The Price Performance of Performance Models\" by Felix Wolf, Technical University of Darmstadt<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Abstract<\/strong>. To understand the scaling behavior of HPC applications, developers often use performance models. A performance model is a formula that expresses a critical performance metric, such as runtime, as a function of one or more execution parameters, such as core count and input size. Performance models offer quick insights on a very high level of abstraction, including predictions of future behavior. Given the complexity of today\u2019s applications, which often combine several sophisticated algorithms, creating performance models manually is extremely laborious. Empirical performance modeling, the process of learning such models from performance data, offers a convenient alternative but comes with its own set of challenges. The two most prominent ones are noise and the cost of the experiments needed to generate the underlying data. In this talk, we will review the state of the art in empirical performance modeling and investigate how we can employ machine learning and other strategies to improve the quality and lower the cost of the resulting models.<\/p><p><strong>Biography<\/strong>. Felix Wolf is a full professor at the Department of Computer Science of the Technical University of Darmstadt in Germany, where he leads the Laboratory for Parallel Programming. He works on methods, tools, and algorithms that support developing and deploying parallel software systems in various life-cycle stages. Wolf received his Ph.D. degree from RWTH Aachen University in 2003. After working more than two years as a postdoc at the Innovative Computing Laboratory of the University of Tennessee, he was appointed research group leader at Juelich Supercomputing Centre. Between 2009 and 2015, he was head of the Laboratory for Parallel Programming at the German Research School for Simulation Sciences in Aachen and a full professor at RWTH Aachen University. Wolf has made major contributions to several open-source performance tools for parallel programs, including Scalasca, Score-P, and Extra-P. Moreover, he has initiated the Virtual Institute \u2013 High Productivity Supercomputing, an international initiative of HPC programming-tool builders aimed at enhancing, integrating, and deploying their products. He has published over 150 refereed articles on parallel computing, several of which have received awards.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-bookmark\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M336 0H48C21.49 0 0 21.49 0 48v464l192-112 192 112V48c0-26.51-21.49-48-48-48zm0 428.43l-144-84-144 84V54a6 6 0 0 1 6-6h276c3.314 0 6 2.683 6 5.996V428.43z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">3:30 pm | Gold Sponsor Talks: \"Better Together- EVIDEN and DDN\" by Genaro Costa (EVIDEN) and Morris Skupinsky (DDN)<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p class=\"p1\"><b>Genaro Costa<\/b>\u00a0<br \/>HPC &amp; AI Solution Architect | EVIDEN<\/p><p class=\"p1\">\u00a0<\/p><p class=\"p1\">\u00a0<\/p><p class=\"p1\"><b>Morris Skupinsky<\/b><\/p><p class=\"p1\">HPC &amp; AI Solution Architect | DDN<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-coffee\" viewBox=\"0 0 640 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M192 384h192c53 0 96-43 96-96h32c70.6 0 128-57.4 128-128S582.6 32 512 32H120c-13.3 0-24 10.7-24 24v232c0 53 43 96 96 96zM512 96c35.3 0 64 28.7 64 64s-28.7 64-64 64h-32V96h32zm47.7 384H48.3c-47.6 0-61-64-36-64h583.3c25 0 11.8 64-35.9 64z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">3:40 pm | Coffee Break<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>Served in the <strong>Legacy Suite<\/strong><\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-layer-group\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M12.41 148.02l232.94 105.67c6.8 3.09 14.49 3.09 21.29 0l232.94-105.67c16.55-7.51 16.55-32.52 0-40.03L266.65 2.31a25.607 25.607 0 0 0-21.29 0L12.41 107.98c-16.55 7.51-16.55 32.53 0 40.04zm487.18 88.28l-58.09-26.33-161.64 73.27c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.51 209.97l-58.1 26.33c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 276.3c16.55-7.5 16.55-32.5 0-40zm0 127.8l-57.87-26.23-161.86 73.37c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.29 337.87 12.41 364.1c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 404.1c16.55-7.5 16.55-32.5 0-40z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">4:20 pm | Poster Session<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>4:20 PM - 6:00 PM | Legacy Suite<\/p><ol><li><strong>Comparative Performance Analysis of DNA Sequence Encoding Methods for Machine Learning-Based Bacterial Classification<\/strong>, <em>Diego Santibanez Oyarce (Universidad Tecnol\u00f3gica Metropolitana, Santiago, Chile), Jorge Vergara-Quezada (Universidad Tecnol\u00f3gica Metropolitana, Santiago, Chile), Ana Moya-Beltr\u00e1n (Universidad Tecnol\u00f3gica Metropolitana, Santiago,Chile)<\/em><\/li><li><strong>Application of Language Models for the Functional Annotation of Conserved Domains in Biological Data<\/strong>, Hugo Osses Prado (Universidad Tecnol\u00f3gica Metropolitana, Santiago, Chile), Ra\u00fal Caulier-Cisterna (Universidad Tecnol\u00f3gica Metropolitana, Santiago, Chile), Ana Moya-Beltr\u00e1n (Universidad Tecnol\u00f3gica Metropolitana, Santiago ,Chile)<\/li><li><strong>Optimizing path analysis in multi-perspective graphs: A study on the migration from NetworkX to graph-tool<\/strong>, <em>Welber P. Ferreira (LNCC, Brazil), Ant\u00f4nio T. A. Gomes (LNCC, Brazil)<\/em><\/li><li><strong>LUAD-SynthNet: Generative Adversarial Networks for Synthetic Single-Cell Transcriptomics in Lung Adenocarcinoma<\/strong>, <em>Joaqu\u00edn Araya-Bustos (Universidad Tecnol\u00f3gica Metropolitana, Chile), Welinton Barrera-Mondaca (Universidad Tecnol\u00f3gica Metropolitana, Chile), Renato \u00c1lvarez-Ramos (Universidad Tecnol\u00f3gica Metropolitana, Chile), Claudia Cancino-Quiroz (Universidad Tecnol\u00f3gica Metropolitana, Chile), Jorge Vergara-Quezada (Universidad Tecnol\u00f3gica Metropolitana, Chile), Ana Moya-Beltr\u00e1n (Universidad Tecnol\u00f3gica Metropolitana, Chile)<\/em><\/li><li><strong>Performance-Guided Evaluation of Clustering Strategies for Single-Cell RNA Sequencing in Cancer Research within HPC Environments<\/strong>, <em>Welinton Barrera-Mondaca (Universidad Tecnol\u00f3gica Metropolitana, Chile), Joaqu\u00edn Araya-Bustos (Universidad Tecnol\u00f3gica Metropolitana, Chile), Renato \u00c1lvarez-Ramos (Universidad Tecnol\u00f3gica Metropolitana, Chile), Claudia Cancino-Quiroz (Universidad Tecnol\u00f3gica Metropolitana, Chile), Ra\u00fal Caulier-Cisterna (Universidad Tecnol\u00f3gica Metropolitana, Chile), Ana Moya-Beltr\u00e1n (Universidad Tecnol\u00f3gica Metropolitana, Chile)<\/em><\/li><li><strong>Landscape of Machine Learning Methods and Data Representations for Antimicrobial Resistance: Toward a Benchmarking Framework in HPC Environments<\/strong>, <em>Camilo Cerda Sarabia (Universidad Tecnol\u00f3gica Metropolitana, Chile), Fernanda Bravo Cornejo (Universidad Tecnol\u00f3gica Metropolitana, Chile), Bel\u00e9n D\u00edaz D\u00edaz (Universidad Tecnol\u00f3gica Metropolitana, Chile), Fausto Cabezas-Mera (Universidad Tecnol\u00f3gica Metropolitana, Chile), Jorge Vergara-Quezada (Universidad Tecnol\u00f3gica Metropolitana, Chile), Ana Moya-Beltr\u00e1n (Universidad Tecnol\u00f3gica Metropolitana, Chile)<\/em><\/li><li><strong>GenomeDefender: Validated High-Precision Detection of Data Poisoning Attacks in Single-Cell RNA-seq Data using a Multi-Model Ensemble<\/strong>, <em>Renato \u00c1lvarez Ramos (Universidad Tecnol\u00f3gica Metropolitana, Chile), Claudia Cancino Quiroz (Universidad Tecnol\u00f3gica Metropolitana, Chile), Joaqu\u00edn Araya Bustos (Universidad Tecnol\u00f3gica Metropolitana, Chile), Welinton Barrera Mondaca (Universidad Tecnol\u00f3gica Metropolitana, Chile), Ana Moya-Beltr\u00e1n (Universidad Tecnol\u00f3gica Metropolitana, Chile), Victor Escobar Jeria (Universidad Tecnol\u00f3gica Metropolitana, Chile)<\/em><\/li><li><strong>High-Performance Computing Evaluation of GATK and Parabricks for Genetic Biomarker Detection in Cancer using scRNA-seq<\/strong>, <em>Claudia Cancino-Quiroz (Universidad Tecnol\u00f3gica Metropolitana, Chile), Renato Alvarez-Ramos (Universidad Tecnol\u00f3gica Metropolitana, Chile), Welinton Barrera-Mondaca (Universidad Tecnol\u00f3gica Metropolitana, Chile), Joaqu\u00edn Araya-Bustos (Universidad Tecnol\u00f3gica Metropolitana, Chile), Victor Escobar (Universidad Tecnol\u00f3gica Metropolitana, Santiago,Chile), Ana Moya-Beltr\u00e1n (Universidad Tecnol\u00f3gica Metropolitana, Chile)<\/em><\/li><li><strong>Aerodynamic design and CFD simulation of a compact car using OpenFOAM: A case study from the city of Bucaramanga, Colombia<\/strong>, <em>Adrian Vargas-Lizarazo (Universidad Industrial de Santander, Colombia), Jorge Luis Chac\u00f3n-Velasco (Universidad Industrial de Santander, Colombia)<\/em><\/li><li><strong>Qualitative assessment of High-Performance Computing (HPC) ecosystem in Panama: needs and potential users<\/strong>,<em> Ivan Bonilla (Universidad Tecnol\u00f3gica de Panam\u00e1, Panam\u00e1), Esteban Meneses (Centro Nacional de Computaci\u00f3n Avanzada, Costa Rica), Reinhardt Pinz\u00f3n (Universidad Tecnol\u00f3gica de Panam\u00e1, Panam\u00e1)<\/em><\/li><li><strong>Instructional Code Editing Using Transformer Models<\/strong>,<em> Yadiel Mercado (University of Puerto Rico at Rio Piedras, Puerto Rico), Michael Alvarez (University of Puerto Rico at Rio Piedras, Puerto Rico)<\/em><\/li><li><strong>High Performance Computing (HPC) Applied in a Hydrological Study in Panama: The Case of the Upper Watershed of the Chagres River<\/strong>, <em>Melanie Quiroz (Universidad Tecnol\u00f3gica de Panam\u00e1, Panam\u00e1), Miguel Salceda (CEMCIT-AIP, Panam\u00e1), Yolanda V\u00e1zquez (Universidad Tecnol\u00f3gica de Panam\u00e1, Panam\u00e1), Milena Zambrano (Universidad Tecnol\u00f3gica de Panam\u00e1, Panam\u00e1), Iris Arjona (Universidad Tecnol\u00f3gica de Panam\u00e1, Panam\u00e1), Xavier Trujillo (Universidad Tecnol\u00f3gica de Panam\u00e1, Panam\u00e1), Javier S\u00e1nchez-Gal\u00e1n (CEMCIT-AIP, Panam\u00e1), Jos\u00e9 F\u00e1brega (CEMCIT-AIP, Panam\u00e1), Reinhardt Pinz\u00f3n (CEMCIT-AIP, Panam\u00e1), Johansell Villalobos (Centro Nacional de Alta Tecnolog\u00eda, Costa Rica), Esteban Meneses (Centro Nacional de Alta Tecnolog\u00eda, Costa Rica) , Carlos Rudamas (Universidade de El Salvador, El Salvador)<\/em><\/li><li><strong>Computer Vision and Artificial Intelligence in Sports Performance Analysis<\/strong>, <em>Gabriel Torres (University of Puerto Rico at Rio Piedras, Puerto Rico), Carlos Vazquez (University of Puerto Rico at Rio Piedras), Javier Osorio (Universidad Manuela Beltr\u00e1n), Edusmildo Orozco (University of Puerto Rico at Rio Piedras, Puerto Rico), Michael Alvarez (University of Puerto Rico at Rio Piedras, Puerto Rico)<\/em><\/li><\/ol><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-glass-whiskey\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M480 32H32C12.5 32-2.4 49.2.3 68.5l56 356.5c4.5 31.5 31.5 54.9 63.4 54.9h273c31.8 0 58.9-23.4 63.4-54.9l55.6-356.5C514.4 49.2 499.5 32 480 32zm-37.4 64l-30 192h-313L69.4 96h373.2z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">7:30 pm | Reception<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>Venue: Talk of the Town<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                            <\/div>\n        \t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-1712837875\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-1712837875\" data-tab-index=\"5\" style=\"--n-tabs-title-order: 5;\" class=\" elementor-element elementor-element-14aa347 e-flex e-con-boxed e-con e-child\" data-id=\"14aa347\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-ef285a1 e-flex e-con-boxed e-con e-child\" data-id=\"ef285a1\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d1758eb elementor-widget elementor-widget-text-editor\" data-id=\"d1758eb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Location: Talk of the Town<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bef7eba wpr-advanced-accordion-icon-side-box elementor-widget elementor-widget-wpr-advanced-accordion\" data-id=\"bef7eba\" data-element_type=\"widget\" data-settings=\"{&quot;active_item&quot;:0}\" data-widget_type=\"wpr-advanced-accordion.default\">\n\t\t\t\t\t\n            <div class=\"wpr-advanced-accordion\" data-accordion-type=\"accordion\" data-active-index=\"0\" data-accordion-trigger=\"click\" data-interaction-speed=\"0.2\">\n\n\t\t\t\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-door-open\" viewBox=\"0 0 640 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M624 448h-80V113.45C544 86.19 522.47 64 496 64H384v64h96v384h144c8.84 0 16-7.16 16-16v-32c0-8.84-7.16-16-16-16zM312.24 1.01l-192 49.74C105.99 54.44 96 67.7 96 82.92V448H16c-8.84 0-16 7.16-16 16v32c0 8.84 7.16 16 16 16h336V33.18c0-21.58-19.56-37.41-39.76-32.17zM264 288c-13.25 0-24-14.33-24-32s10.75-32 24-32 24 14.33 24 32-10.75 32-24 32z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">9:00 am | Welcome \u2013 CARLA 2026 Announcement<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Venue<\/strong>. Talk of the Town<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-layer-group\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M12.41 148.02l232.94 105.67c6.8 3.09 14.49 3.09 21.29 0l232.94-105.67c16.55-7.51 16.55-32.52 0-40.03L266.65 2.31a25.607 25.607 0 0 0-21.29 0L12.41 107.98c-16.55 7.51-16.55 32.53 0 40.04zm487.18 88.28l-58.09-26.33-161.64 73.27c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.51 209.97l-58.1 26.33c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 276.3c16.55-7.5 16.55-32.5 0-40zm0 127.8l-57.87-26.23-161.86 73.37c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.29 337.87 12.41 364.1c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 404.1c16.55-7.5 16.55-32.5 0-40z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">9:20 am | Research Paper Session 4: Performance and Energy Efficiency Optimization<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>9:20 AM - 10:40 AM<br \/>Chair: <span data-olk-copy-source=\"MessageBody\">Silvio Rizzi<\/span><\/p><ol><li><em>NUMA-Aware FIFO Scheduling: Optimizing Data Movement for the Montage Workflow<\/em><\/li><li><em>Subgroup and SIMD Optimization of RTM Kernels in Intel SYCL for Portable Performance<\/em><\/li><li><em>Leveraging Local Data Share for Efficient Stencil Computation in the Fletcher Model on AMD MI250X<\/em><\/li><li><em>Fast Sorting for the RISC-V \u2018V\u2019 Vector Extension<\/em><\/li><\/ol><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">9:20 am | Paper: \"NUMA-Aware FIFO Scheduling: Optimizing Data Movement for the Montage Workflow\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors. <\/strong><em>Aurelio Vivas (Universidad de los Andes); Harold Castro (Uniandes)<\/em><\/p><p><strong>Abstract.<\/strong> High-performance computing systems are essential for efficient scientific work flow execution. However, integrating workflow scheduling algorithms with non-uniform memory access (NUMA) architectures remains largely unexplored. This work extends an existing FIFO scheduling algorithm by incorporating NUMA awareness to reduce data movement. The approach leverages a runtime system that uses the Portable Hardware Locality (hwloc) library to map memory topology and collect task execution metadata, such as core and memory locality, which the scheduler uses to make NUMA- and data locality-aware decisions. The proposed scheduler achieved 64.72%, 65.79%, and 70.17% local read accesses for the 619-, 310-, and 58-task Montagework flows,respectively, improving average task read times and balancing the distribution of tasks, data, and memory accesses. These results demonstrate the effectiveness of the strategy, particularly for larger workflows.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">9:40 am | Paper: \"Subgroup and SIMD Optimization of RTM Kernels in Intel SYCL for Portable Performance\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Cristiano Alex K\u00fcnas (Federal University of Rio Grande do Sul - UFRGS); Gabriel Freytag (Federal University of Rio Grande do Sul - UFRGS); Everton Paulino (Intel Labs); Fabio Zuvanov (Intel Labs); Alexandre Sardinha (Petrobras); Philippe Navaux (Federal University of Rio Grande do Sul - UFRGS); Alexandre Carissimi (Federal University of Rio Grande do Sul - UFRGS)<\/em><\/p><p><strong>Abstract.<\/strong> Reverse Time Migration (RTM) is a key method for seismic imaging, but it demands substantial computational power and memory. With the growing diversity of GPU architectures, ensuring performance portability while maintaining energy efficiency has become a major challenge.This work evaluates RTM simulations on the Intel Max 1100 GPU, comparing three implementations: a baseline version, a SIMD-optimized version tailored for Intel\u2019s architecture, and a subgroup-based version designed for portability. Experiments across multiple 3D grid sizes and simulation durations assess performance, energy consumption, and computational efficiency. Results show that SIMD optimizations offer the best performance (up to 4.6\u00d7) and energy savings (up to 36%) but limit portability. In contrast, subgroup-based optimizations strike a balance, delivering speedups over the baseline while maintaining broader hardware compatibility. These findings suggest practical strategies for deploying efficient and portable RTM simulations, particularly valuable for heterogeneous HPC environments in cloud platforms and collaborative industrial workflows where code sustainability and adaptability are critical.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">10:00 am | Paper: \"Leveraging Local Data Share for Efficient Stencil Computation in the Fletcher Model on AMD MI250X\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Arthur Lorenzon (Federal University of Rio Grande do Sul - UFRGS); Alexandre Sardinha (Petrobras); Philippe Navaux (Federal University of Rio Grande do Sul - UFRGS)<\/em><\/p><p><strong>Abstract.<\/strong> As high-performance computing systems scale, maximizing both performance and energy efficiency has become critical, particularly for memory-bound stencil computations that dominate scientific applications. In this scenario, we investigate optimization techniques for the Fletcher model, a high-order finite-difference seismic wave propagation solver, on AMD GPUs. We consider the impact of two hardware-level strategies: (i) exploiting Local Data Share (LDS) memory to reduce redundant global memory accesses in stencil kernels, and (ii) applying non-temporal memory instructions to avoid cache pollution from low-reuse coefficient arrays. The optimizations target the two main kernels of the model, partialDerivatives and propagation, by reusing shared memory in the y- and z-directions, improving spatial locality, and reducing memory traffic. Through a set of experiments performed on an AMD MI250X, we show that using LDS improves performance and energy by 9.3% and 12% compared to the baseline version that exploits the use of global memory. Also, when considering both LDS and non-temporal stores together, the performance gains increase to 31.9% while the energy savings increase to 27%, compared to the baseline version.\u00a0<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">10:20 am | Paper: \"Fast Sorting for the RISC-V \u2018V\u2019 Vector Extension\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Daniel Salmun (Universidad de Buenos Aires); Esteban Mocskos (Universidad de Buenos Aires &amp; CONICET)<\/em><\/p><p><strong>Abstract.<\/strong> The increasing adoption of the RISC-V instruction set architecture (ISA), particularly its \u201cV\u201d Vector (RVV) extension, introduces new opportunities and challenges for software optimization. This paper tackles high-performance sorting on RISC-V processors by developing a native, in-place, single-threaded vectorized Quicksort algorithm specifically optimized for the RVV extension. We investigate key optimization techniques, including (1) LMUL-aware register grouping to maximize throughput, (2) arithmetic workarounds for RVV\u2019s lack of interleaving instructions, and (3) strategic instruction reordering to mitigate hazards. Comprehensive experimental evaluations on a SpacemiT K1 processor (RV64GCVB, 256-bit VLEN) demonstrate superior performance: for large datasets of 32-bit integers, our implementation significantly outperforms all compared state-of-the-art sorting implementations, achieving a speedup of up to 1.89 times over the fastest alternative. These findings highlight RVV\u2019s potential for computationally intensive tasks and offer insights into overcoming its unique architectural challenges, such as implementation-defined register widths and variations in available vector instructions compared to other SIMD platforms.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-coffee\" viewBox=\"0 0 640 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M192 384h192c53 0 96-43 96-96h32c70.6 0 128-57.4 128-128S582.6 32 512 32H120c-13.3 0-24 10.7-24 24v232c0 53 43 96 96 96zM512 96c35.3 0 64 28.7 64 64s-28.7 64-64 64h-32V96h32zm47.7 384H48.3c-47.6 0-61-64-36-64h583.3c25 0 11.8 64-35.9 64z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">10:40 am | Coffee Break<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>Served in the <strong>Talk of the Town<\/strong><\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-layer-group\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M12.41 148.02l232.94 105.67c6.8 3.09 14.49 3.09 21.29 0l232.94-105.67c16.55-7.51 16.55-32.52 0-40.03L266.65 2.31a25.607 25.607 0 0 0-21.29 0L12.41 107.98c-16.55 7.51-16.55 32.53 0 40.04zm487.18 88.28l-58.09-26.33-161.64 73.27c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.51 209.97l-58.1 26.33c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 276.3c16.55-7.5 16.55-32.5 0-40zm0 127.8l-57.87-26.23-161.86 73.37c-7.56 3.43-15.59 5.17-23.86 5.17s-16.29-1.74-23.86-5.17L70.29 337.87 12.41 364.1c-16.55 7.5-16.55 32.5 0 40l232.94 105.59c6.8 3.08 14.49 3.08 21.29 0L499.59 404.1c16.55-7.5 16.55-32.5 0-40z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">11:20 am | Research Paper Session 5: Emerging Technologies<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>11:20 AM - 12:00 PM<br \/>Chair: <span data-olk-copy-source=\"MessageBody\">Arthur Lorenzon<\/span><\/p><ol><li><em>ACCLAIM: Accelerating Long Context LLM Inference on Heterogeneous Edge Platforms<\/em><\/li><li><em>A Scientific Data Integrity system based on Blockchain<\/em><\/li><\/ol><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">11:20 am | Paper: \"ACCLAIM: Accelerating Long Context LLM Inference on Heterogeneous Edge Platforms\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors. <\/strong><em>Rakshith Jayanth (University of Southern California); Yi Chien Lin (University of Southern California); Souvik Kundu (Intel Labs); Deepak A Mathaikutty (Intel Labs); Viktor Prasanna (University of Southern California)<\/em><\/p><p><strong>Abstract. <\/strong>With the growing deployment of Large Language Models (LLMs) on edge platforms, supporting long-context inference has become increasingly important. However, achieving low inference latency remains challenging due to high computational and data transfer demands, combined with limited compute and memory resources. In this work, we present ACCLAIM, a novel system designed for long-context inference on heterogeneous edge platforms. ACCLAIM addresses key challenges by: (1) segmenting the prefill stage to generate and access KV cache in chunks, reducing memory footprint; (2) leveraging the inherent sparsity in self-attention to lower computational complexity; and (3) performing offline profiling to determine optimal chunk size and generate an efficient load-balancing strategy across heterogeneous cores. These optimizations significantly reduce Time To First Token (TTFT). We evaluate ACCLAIM on two state-of-the-art heterogeneous platforms, showing up to 23\u00d7 speedup in TTFT over standalone chunked prefill and up to 1.5\u00d7 average speedup over state-of-the-art mapping algorithms.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"far fa-edit\"><\/i>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">11:40 am | Paper: \"A Scientific Data Integrity system based on Blockchain\"<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Authors.<\/strong> <em>Gian Sebastian Mier Bello (Universidad Industrial de Santander - UIS); Carlos Jaime Barrios Hernandez (Universidad Industrial de Santander - UIS); Alexander Mart\u00ednez M\u00e9ndez (Universidad Industrial de Santander - UIS); Robinson Rivas (Universidad Central de Venezuela); Luis Alberto Nu\u00f1ez Villavicencio (Universidad Industrial de Santander - UIS)<\/em><\/p><p><strong>Abstract.<\/strong> In most High Performance Computing (HPC) projects nowadays, there is a lot of data obtained from different sources, depending on the project's objectives. Some of that data is very huge in terms of size, so copying such data sometimes is an unrealistic goal. On the other hand, science requires data used for different purposes to remain unaltered, so different groups of researchers can reproduce results, discuss theories, and validate each other. In this paper, we present a novel approach to help research groups to validate data integrity on such distributed repositories using Blockchain. Originally developed for cryptographic currencies, Blockchain has demonstrated a versatile range of uses. Our proposal ensures 1) secure access to data management, 2) easy validation of data integrity, and 3) an easy way to add new records to the dataset with the same robust integrity policy. A prototype was developed and tested using a subset of a public dataset from a real scientific collaboration, the Latin American Giant Observatory (LAGO) Project.<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-people-arrows\" viewBox=\"0 0 576 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M96,128A64,64,0,1,0,32,64,64,64,0,0,0,96,128Zm0,176.08a44.11,44.11,0,0,1,13.64-32L181.77,204c1.65-1.55,3.77-2.31,5.61-3.57A63.91,63.91,0,0,0,128,160H64A64,64,0,0,0,0,224v96a32,32,0,0,0,32,32V480a32,32,0,0,0,32,32h64a32,32,0,0,0,32-32V383.61l-50.36-47.53A44.08,44.08,0,0,1,96,304.08ZM480,128a64,64,0,1,0-64-64A64,64,0,0,0,480,128Zm32,32H448a63.91,63.91,0,0,0-59.38,40.42c1.84,1.27,4,2,5.62,3.59l72.12,68.06a44.37,44.37,0,0,1,0,64L416,383.62V480a32,32,0,0,0,32,32h64a32,32,0,0,0,32-32V352a32,32,0,0,0,32-32V224A64,64,0,0,0,512,160ZM444.4,295.34l-72.12-68.06A12,12,0,0,0,352,236v36H224V236a12,12,0,0,0-20.28-8.73L131.6,295.34a12.4,12.4,0,0,0,0,17.47l72.12,68.07A12,12,0,0,0,224,372.14V336H352v36.14a12,12,0,0,0,20.28,8.74l72.12-68.07A12.4,12.4,0,0,0,444.4,295.34Z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">12:00 pm | Panel: \u201cAI, National Sovereignty, and Sustainability\u201d<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Moderator<\/strong>: Addison Snell, Intersect360 Research<\/p><p>Panelists:<\/p><ul><li>Bernd Mohr, J\u00fclich Supercomputing Centre<\/li><li>Ver\u00f3nica Melesse Vergara, Oak Ridge National Laboratory<\/li><li><span class=\"outlook-search-highlight\" data-markjs=\"true\">Carlos<\/span> Jaime Barrios Hernandez, SCALAC<\/li><\/ul><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-users\" viewBox=\"0 0 640 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M96 224c35.3 0 64-28.7 64-64s-28.7-64-64-64-64 28.7-64 64 28.7 64 64 64zm448 0c35.3 0 64-28.7 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208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p><strong>Best Poster Award<\/strong> \"Comparative Performance Analysis of DNA Sequence Encoding Methods for Machine Learning-Based Bacterial Classification\"<br \/>Diego Santib\u00e1\u00f1ez Oyarce, Esteban G\u00f3mez Ter\u00e1n, Jorge Vergara-Quezada, Ana Moya-Beltr\u00e1n<br \/><br \/><\/p><p><strong>Best Paper Award<\/strong><br \/>\"Fast Sorting for the RISC-V \u2018V\u2019 Vector Extension\"<br \/>Daniel Salmun, Esteban Mocskos<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                \n\t\t\t\t\t<div class=\"wpr-accordion-item-wrap\">\n\t\t\t\t\t\t<button class=\"wpr-acc-button\">\n\t\t\t\t\t\t\t<span class=\"wpr-acc-item-title\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-icon-box\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-title-icon\">\n\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-award\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M97.12 362.63c-8.69-8.69-4.16-6.24-25.12-11.85-9.51-2.55-17.87-7.45-25.43-13.32L1.2 448.7c-4.39 10.77 3.81 22.47 15.43 22.03l52.69-2.01L105.56 507c8 8.44 22.04 5.81 26.43-4.96l52.05-127.62c-10.84 6.04-22.87 9.58-35.31 9.58-19.5 0-37.82-7.59-51.61-21.37zM382.8 448.7l-45.37-111.24c-7.56 5.88-15.92 10.77-25.43 13.32-21.07 5.64-16.45 3.18-25.12 11.85-13.79 13.78-32.12 21.37-51.62 21.37-12.44 0-24.47-3.55-35.31-9.58L252 502.04c4.39 10.77 18.44 13.4 26.43 4.96l36.25-38.28 52.69 2.01c11.62.44 19.82-11.27 15.43-22.03zM263 340c15.28-15.55 17.03-14.21 38.79-20.14 13.89-3.79 24.75-14.84 28.47-28.98 7.48-28.4 5.54-24.97 25.95-45.75 10.17-10.35 14.14-25.44 10.42-39.58-7.47-28.38-7.48-24.42 0-52.83 3.72-14.14-.25-29.23-10.42-39.58-20.41-20.78-18.47-17.36-25.95-45.75-3.72-14.14-14.58-25.19-28.47-28.98-27.88-7.61-24.52-5.62-44.95-26.41-10.17-10.35-25-14.4-38.89-10.61-27.87 7.6-23.98 7.61-51.9 0-13.89-3.79-28.72.25-38.89 10.61-20.41 20.78-17.05 18.8-44.94 26.41-13.89 3.79-24.75 14.84-28.47 28.98-7.47 28.39-5.54 24.97-25.95 45.75-10.17 10.35-14.15 25.44-10.42 39.58 7.47 28.36 7.48 24.4 0 52.82-3.72 14.14.25 29.23 10.42 39.59 20.41 20.78 18.47 17.35 25.95 45.75 3.72 14.14 14.58 25.19 28.47 28.98C104.6 325.96 106.27 325 121 340c13.23 13.47 33.84 15.88 49.74 5.82a39.676 39.676 0 0 1 42.53 0c15.89 10.06 36.5 7.65 49.73-5.82zM97.66 175.96c0-53.03 42.24-96.02 94.34-96.02s94.34 42.99 94.34 96.02-42.24 96.02-94.34 96.02-94.34-42.99-94.34-96.02z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t<span class=\"wpr-acc-title-text\">1:00 pm | Closing<\/span>\n\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-close\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t<span class=\"wpr-toggle-icon wpr-ti-open\"><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t<div class=\"wpr-acc-panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"wpr-acc-panel-content\"><p>1:00 PM - 1:10 PM<\/p><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n                    <\/div>\n\n                            <\/div>\n        \t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Monday Tuesday Wednesday Thursday Friday Tutorials Day | September 22, 2025Click here for session summariesRegistration opens at 8:15 AM in the Grand Jamaica Suite lobby. Track 1Venue: Grand Jamaica (Montego) Suite Track 2Venue: Grand Jamaica (Negril) Suite Track 3Venue: Grand Jamaica (Port Antonio) Suite [Empty] 9:00 am. A Programming Introduction to HPC 9:00 am. Simulating quantum algorithms with Q-Team 11:00 -11:30 am. Coffee Break [Empty] A Programming Introduction to HPC Simulating quantum algorithms with Q-Team 1:00 &#8211; 2:00 pm. Lunch [Empty] 2:00 pm. A Programming Introduction to HPC 2:00 pm. Dist. Deep Learning: A Tutorial on Distributed Training Techniques for Large Deep Learning Models 4:00- 4:30 pm. Coffee Break [Empty] A Programming Introduction to HPC Dist. Deep Learning: A Tutorial on Distributed Training Techniques for Large Deep Learning Models Workshops Day | September 23, 2025Click here for session summariesRegistration opens at 8:30 AM in the Grand Jamaica Suite lobby. Track 1Venue: Grand Jamaica (Montego) Suite Track 2Venue: Grand Jamaica (Negril) Suite Track 3Venue: Grand Jamaica (Port Antonio) Suite Track 4Venue: Belisario Suite 9:00 am. Latin America and Caribbean Advances on Weather Forecasting 9:00 am. Energy Efficiency and Sustainability in AI, HPC, and Quantum Computing 9:00 am. HPC Centers Around the World \u2013 Science and Computing Perspectives for the Latin American and Caribbean Community 9:00 am. BioCARLA 2025 \u2013 Challenges and Advances in Small\/Foundation Models for Biomedical and Life Sciences 11:00 &#8211; 11:30 am. Coffee Break Latin America and Caribbean Advances on Weather Forecasting Energy Efficiency and Sustainability in AI, HPC, and Quantum Computing HPC Centers Around the World \u2013 Science and Computing Perspectives for the Latin American and Caribbean Community BioCARLA 2025 \u2013 Challenges and Advances in Small\/Foundation Models for Biomedical and Life Sciences 1:00 &#8211; 2:00 pm. Lunch Latin America and Caribbean Advances on Weather Forecasting Energy Efficiency and Sustainability in AI, HPC, and Quantum Computing Advanced Computing Trends in Latin America, the Caribbean, and the World Workshop Women in High Performance Computing (WHPC) 4:00 &#8211; 4:30 am. Coffee Break Latin America and Caribbean Advances on Weather Forecasting Energy Efficiency and Sustainability in AI, HPC, and Quantum Computing Advanced Computing Trends in Latin America, the Caribbean, and the World Workshop Women in High Performance Computing (WHPC) Location: Grand Jamaica SuiteRegistration opens at 8:30 AM in the Grand Jamaica Suite lobby. 9:00 am | Opening Session Dr. Vanesa M. Tennant Williams, Moderator CARLA 2025 Welcome MessageDr. Kevin A. BrownGeneral Co-ChairCARLA 2025\u00a0 RemarksDr. Charah T. WatsonExecutive DirectorScientific Research Council Prof. Tannecia StephensonDeputy Dean, Faculty of Science and technologyCo-Director of the Climate Studies group, Mona (CSGM) University of the West Indies, Mona Mrs. Anika C. D. ShuttleworthChief Information OfficerJAMICTA &#8211; ICT Authority Dr. Carlos Jaime Barrios HernandezGeneral ChairSCALAC 9:30 am | Invited Talk: &#8220;Exploration Technologies to Enable NASA Missions&#8221; by Rupak Biswas, NASA Abstract.\u00a0NASA\u2019s missions span human and robotic space exploration, ground-breaking aeronautics research, and Earth and space sciences. This talk will provide a broad overview of numerous HPC and related technologies that NASA develops, adapts, and implements for its wide spectrum of programs and projects ranging from Earth to deep space. Biography.\u00a0Dr. Rupak Biswas is currently the Director of Exploration Technology at NASA Ames Research Center, Moffett Field, Calif., and has held this Senior Executive Service (SES) position since January 2016. In this role, he is in charge of planning, directing, and coordinating the technology development and operational activities of the organization that comprises of advanced supercomputing, human systems integration, intelligent systems, and entry systems technology. The directorate consists of approximately 700 employees with an annual budget of $160 million, and includes two of NASA\u2019s critical and consolidated infrastructures: arcjet testing facility and supercomputing facility. He is also the Manager of the High End Computing Capability Project that provides a full range of advanced computational resources and services to numerous NASA programs. In addition, he leads the emerging quantum computing effort for NASA. Dr. Biswas received his Ph.D. in Computer Science from Rensselaer in 1991, and has been at NASA ever since. During this time, he has received several agency awards, including the Exceptional Achievement Medal and the Outstanding Leadership Medal. He is an internationally recognized expert in high performance computing and has published more than 150 technical papers, received many Best Paper awards, edited several journal special issues, and given numerous lectures around the world. 10:30 am | Diamond Sponsor Talk: What you \u201dREALLY\u201d need to know about #AI (from AI to AgenticAI) by Francisco Aguirre (Dell Technologies) &#038; Pedro Mario Cruz e Silva (NVIDIA) What you &#8220;REALLY&#8221; need to know about #AI (from AI to AgenticAI)by Francisco Aguirre (Dell Technologies) &amp; Pedro Mario Cruz e Silva \u00a0(NVIDIA) Francisco Aguirre, LATAM NVIDIA Solutions Senior Principal, Dell Technologies Francisco Aguirre is an experienced technology leader with more than 30 years in the IT industry, specializing in Artificial Intelligence, High-Performance Computing, and emerging technologies. He currently leads NVIDIA solutions for Dell Technologies in Latin America, helping organizations harness the power of accelerated computing to drive innovation and competitive advantage. Throughout his career, Francisco has advised clients across key industries\u2014including finance, telecommunications, retail, airlines, and education\u2014on how to adopt and scale transformative technologies. His expertise spans from data analytics, business intelligence, and big data, to modern AI deployments leveraging NVIDIA platforms, GPU-based architectures, and hybrid cloud strategies. Francisco is recognized as a trusted advisor, speaker, and thought leader. He has delivered keynotes and technical sessions at major industry events such as Dell Technologies World, Dell Technologies Forum, and Mexico Business Forum. His presentations focus on making cutting-edge concepts like Generative AI, Retrieval-Augmented Generation, Agentic AI, and Quantum Computing accessible to both business and technical audiences. He holds a degree in Systems and Computer Science Engineering from La Salle University and a master\u2019s degree in Customer Relationship Management from Duke University. Passionate about innovation, Francisco continues to drive conversations at the intersection of technology, business, and culture. Pedro M\u00e1rio Cruz e Silva, Senior Solutions Architect | NVIDIA Latin America Pedro M\u00e1rio Cruz e Silva did his BSc (1995), and MSc (1998) at Federal<\/p>\n","protected":false},"author":6,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-692","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/carlaconference.org\/wp-json\/wp\/v2\/pages\/692","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/carlaconference.org\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/carlaconference.org\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/carlaconference.org\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/carlaconference.org\/wp-json\/wp\/v2\/comments?post=692"}],"version-history":[{"count":496,"href":"https:\/\/carlaconference.org\/wp-json\/wp\/v2\/pages\/692\/revisions"}],"predecessor-version":[{"id":1485,"href":"https:\/\/carlaconference.org\/wp-json\/wp\/v2\/pages\/692\/revisions\/1485"}],"wp:attachment":[{"href":"https:\/\/carlaconference.org\/wp-json\/wp\/v2\/media?parent=692"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}