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DTSTART;TZID=America/Denver:20231114T100000
DTEND;TZID=America/Denver:20231114T170000
UID:submissions.supercomputing.org_SC23_sess291_rpost217@linklings.com
SUMMARY:Evaluating Performance Portability of GPU Programming Models
DESCRIPTION:Joshua H. Davis, Pranav Sivaraman, Isaac Minn, and Abhinav Bha
 tele (University of Maryland)\n\nMaintaining a single codebase that can ac
 hieve good performance on a range of accelerator-based supercomputing plat
 forms is of extremely high value for productive scientific application dev
 elopment. However, the large quantity of programming models available whic
 h claim to provide performance portability leaves developers with a comple
 x choice when picking a model to use, potentially requiring an intensive e
 ffort to test each available model with kernels from their app. In order t
 o better understand the current state of performance portable programming 
 models, this project evaluates seven of the most popular programming model
 s using two memory-bound mini-applications on two leadership-class superco
 mputers, Summit and Perlmutter. These results provide a useful evaluation 
 of how well each programming model provides true performance portability i
 n real-world usage for memory-bound applications.\n\nTag: Artificial Intel
 ligence/Machine Learning, Architecture and Networks, Heterogeneous Computi
 ng, I/O and File Systems, Performance Measurement, Modeling, and Tools, Po
 st-Moore Computing, Programming Frameworks and System Software, Quantum Co
 mputing\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass
 \n\n
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