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UID:submissions.supercomputing.org_SC24_sess533_post223@linklings.com
SUMMARY:PerfFlowAspect: A User-Friendly Performance Tool for Scientific Wo
 rkflows
DESCRIPTION:Aliza Lisan (University of Oregon), Tapasya Patki and Stephani
 e Brink (Lawrence Livermore National Laboratory (LLNL)), Zhiwei Yang and S
 pencer Greene (Worcester Polytechnic Institute (WPI)), Konstantinos Parasy
 ris (Lawrence Livermore National Laboratory (LLNL)), Shubbhi Taneja (Worce
 ster Polytechnic Institute (WPI)), and Hank Childs (University of Oregon)\
 n\nIn scientific computing, artificial intelligence and/or machine learnin
 g (AI/ML) are appearing increasingly often in scientific workflows on supe
 rcomputers, due to their ability to solve more complex problems. With resp
 ect to performance analysis tools, the nature of these workflows creates n
 ew requirements for performance analysis tools, in particular incentivizin
 g lower integration costs and support for more diverse codes.\n\nIn respon
 se, we introduce PerfFlowAspect, which approaches this problem via reduced
  instrumentation costs, support for C/C++ and Python code bases, and multi
 ple trace formats that support multiple workflow components. To evaluate i
 ts effectiveness, we consider the use cases of AMS: a complex application 
 to simplify machine learning surrogate model integration in HPC codes.\n\n
 PerfFlowAspect is an open-source tool under active research and developmen
 t. At the poster session, I will present my work with the aid of the poste
 r by elaborating the data, text and figures in it.\n\nRegistration Categor
 y: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha Afza
 l (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen National H
 igh Performance Computing Center); Sally Ellingson (University of Kentucky
 ); and Alan Sussman (University of Maryland)\n\n
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