Presentation
PerfFlowAspect: A User-Friendly Performance Tool for Scientific Workflows
DescriptionIn scientific computing, artificial intelligence and/or machine learning (AI/ML) are appearing increasingly often in scientific workflows on supercomputers, due to their ability to solve more complex problems. With respect to performance analysis tools, the nature of these workflows creates new requirements for performance analysis tools, in particular incentivizing lower integration costs and support for more diverse codes.
In response, we introduce PerfFlowAspect, which approaches this problem via reduced instrumentation costs, support for C/C++ and Python code bases, and multiple trace formats that support multiple workflow components. To evaluate its effectiveness, we consider the use cases of AMS: a complex application to simplify machine learning surrogate model integration in HPC codes.
PerfFlowAspect is an open-source tool under active research and development. At the poster session, I will present my work with the aid of the poster by elaborating the data, text and figures in it.
In response, we introduce PerfFlowAspect, which approaches this problem via reduced instrumentation costs, support for C/C++ and Python code bases, and multiple trace formats that support multiple workflow components. To evaluate its effectiveness, we consider the use cases of AMS: a complex application to simplify machine learning surrogate model integration in HPC codes.
PerfFlowAspect is an open-source tool under active research and development. At the poster session, I will present my work with the aid of the poster by elaborating the data, text and figures in it.

Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
TP
XO/EX
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