Effortless Monitoring of Arithmetic Intensity with PAPI’s Counter Analysis Toolkit

Daniel Barry (), Anthony Danalis () and Heike Jagode ()
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Daniel Barry: University of Tennessee, Innovative Computing Laboratory (ICL)
Anthony Danalis: University of Tennessee, Innovative Computing Laboratory (ICL)
Heike Jagode: University of Tennessee, Innovative Computing Laboratory (ICL)

A chapter in Tools for High Performance Computing 2018 / 2019, 2021, pp 195-218 from Springer

Abstract: Abstract With exascale computing forthcoming, performance metrics such as memory traffic and arithmetic intensity are increasingly important for codes that heavily utilize numerical kernels. Performance metrics in different CPU architectures can be monitored by reading the occurrences of various hardware events. However, from architecture to architecture, it becomes more and more unclear which native performance events are indexed by which event names, making it difficult for users to understand what specific events actually measure. This ambiguity seems particularly true for events related to hardware that resides beyond the compute core, such as events related to memory traffic. Still, traffic to memory is a necessary characteristic for determining arithmetic intensity. To alleviate this difficulty, PAPI’s Counter Analysis Toolkit measures the occurrences of events through a series of benchmarks, allowing its users to discover the high-level meaning of native events. We (i) leverage the capabilities of the Counter Analysis Toolkit to identify the names of hardware events for reading and writing bandwidth utilization in addition to floating-point operations, (ii) measure the occurrences of the events they index during the execution of important numerical kernels, and (iii) verify their identities by comparing these occurrence patterns to the expected arithmetic intensity of the numerical kernels.

Date: 2021
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DOI: 10.1007/978-3-030-66057-4_11

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