{"673964":{"#nid":"673964","#data":{"type":"event","title":"PhD Proposal by Sara Karamati","body":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003ETitle: Application-Driven Solutions for Exploiting Co-processors in High Performance Computing\u003C\/span\u003E\u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003E\u003Cspan\u003ESara Karamati\u003C\/span\u003E\u003C\/strong\u003E\u003Cbr \/\u003E\r\nComputer Science PhD Student\u003Cbr \/\u003E\r\nSchool of Computational Science and Engineering\u003Cbr \/\u003E\r\nGeorgia Institute of Technology\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003E\u003Cspan\u003EDate:\u003C\/span\u003E\u003C\/strong\u003E\u0026nbsp;Wednesday, April 10th, 2024\u003Cbr \/\u003E\r\n\u003Cstrong\u003E\u003Cspan\u003ETime:\u003C\/span\u003E\u003C\/strong\u003E\u0026nbsp;12:00pm \u2013 2:00pm (EDT)\u003Cbr \/\u003E\r\n\u003Cstrong\u003E\u003Cspan\u003EMeeting Link:\u003C\/span\u003E\u003C\/strong\u003E\u0026nbsp;\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/99630881977?pwd=c21jOFllOTF0bU1uYTdsZTFsbGtqZz09\u0022 target=\u0022_blank\u0022\u003EZoom Link\u003C\/a\u003E\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003E\u003Cspan\u003ECommittee:\u003C\/span\u003E\u003C\/strong\u003E\u003Cbr \/\u003E\r\nDr. Richard W. Vuduc (advisor), School of Computational Science and Engineering, Georgia Institute of Technology\u003Cbr \/\u003E\r\nDr. Jeffrey Young, School of Computer Science, Georgia Institute of Technology\u003Cbr \/\u003E\r\nDr. Hyesoon Kim, School of Computer Science, Georgia Institute of Technology\u003Cbr \/\u003E\r\nDr. Spencer Bryngelson, School of Computational Science and Engineering, Georgia Institute of Technology\u003Cbr \/\u003E\r\nDr. Chris Siefert, Sandia National Laboratories\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/strong\u003E\u003Cbr \/\u003E\r\nIn this proposal, we demonstrate novel strategies and insights for optimizing high-performance applications, addressing algorithmic imbalances by tailoring to the unique capabilities of modern co-processors like Graphics Processing Units (GPUs) or Data Processing Units (DPUs). We investigate the unexpected potential of DPUs as compute accelerators in HPC applications far beyond their conventional roles in networking control, storage management, or security.\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\nOur exploration is structured around case studies exemplifying distinct algorithmic complexities: Single Source Shortest Path (SSSP) in graph computing, MiniMD in molecular dynamics simulation, and Maxwell solver in electromagnetics applications. Each study presents challenges in task parallelism, workload distribution, and data dependencies, necessitating precise algorithmic modifications to counteract imbalances and inefficiencies.\u0026nbsp;\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\nThis work unfolds in three pivotal sections, each dedicated to one of the aforementioned case studies. Initially, we delve into optimizing a GPU-based Single-Source Shortest Path (SSSP) algorithm by modulating an algorithmic parameter, delta, for fine-tuning parallelism and power usage, thereby realizing up to 50% speedup and 25% power savings. Secondly, we investigate the optimization of the MiniMD Molecular Dynamics Simulation, proposing a new heuristic devised to enhance task concurrency and a new offloading scenario to DPUs. Despite the DPUs\u0027 typically modest performance, we attain up to 20% performance boost, albeit with a moderate increase in power cost. Finally, we propose a task partitioning strategy for the Maxwell Multigrid Solver, accentuating the practical and theoretical implications of task offloading and partitioning on DPUs, paving the way for an informed, nuanced approach to enhancing computational performance in heterogeneous environments. Finally, through the MiniMD and Maxwell solver studies, we investigate the efficiency and performance of DPUs, leading to a careful examination of the versatility of DPUs across different computational scenarios.\u003C\/span\u003E\u003C\/span\u003E\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EApplication-Driven Solutions for Exploiting Co-processors in High Performance Computing\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Application-Driven Solutions for Exploiting Co-processors in High Performance Computing"}],"uid":"27707","created_gmt":"2024-04-03 17:22:17","changed_gmt":"2024-04-03 17:22:17","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-04-10T12:00:00-04:00","event_time_end":"2024-04-10T14:00:00-04:00","event_time_end_last":"2024-04-10T14:00:00-04:00","gmt_time_start":"2024-04-10 16:00:00","gmt_time_end":"2024-04-10 18:00:00","gmt_time_end_last":"2024-04-10 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ZOOM","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}