Automated Programmatic Performance Analysis of Parallel Programs
CoRR(2024)
摘要
Developing efficient parallel applications is critical to advancing
scientific development but requires significant performance analysis and
optimization. Performance analysis tools help developers manage the increasing
complexity and scale of performance data, but often rely on the user to
manually explore low-level data and are rigid in how the data can be
manipulated. We propose a Python-based API, Chopper, which provides high-level
and flexible performance analysis for both single and multiple executions of
parallel applications. Chopper facilitates performance analysis and reduces
developer effort by providing configurable high-level methods for common
performance analysis tasks such as calculating load imbalance, hot paths,
scalability bottlenecks, correlation between metrics and CCT nodes, and causes
of performance variability within a robust and mature Python environment that
provides fluid access to lower-level data manipulations. We demonstrate how
Chopper allows developers to quickly and succinctly explore performance and
identify issues across applications such as AMG, Laghos, LULESH, Quicksilver
and Tortuga.
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