Enabling Call Path Querying in Hatchet to Identify Performance Bottlenecks in Scientific Applications

Ian Lumsden, Jakob Luettgau, Vanessa Lama, Connor Scully-Allison, Stephanie Brink, Katherine E. Isaacs, Olga Pearce, Michela Taufer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

As computational science applications benefit from larger-scale, more heterogeneous high performance computing (HPC) systems, the process of studying their performance becomes increasingly complex. The performance data analysis library Hatchet provides some insights into this complexity, but is currently limited in its analysis capabilities. Missing capabilities include the handling of relational caller-callee data captured by HPC profilers. To address this shortcoming, we augment Hatchet with a Call Path Query Language that leverages relational data in the performance analysis of scientific applications. Specifically, our Query Language enables data reduction using call path pattern matching. We demonstrate the effectiveness of our Query Language in identifying performance bottlenecks and enhancing Hatchet's analysis capabilities through three case studies. In the first case study, we compare the performance of sequential and multi-threaded versions of the graph alignment application Fido. In doing so, we identify the existence of large memory inefficiencies in both versions. In the second case study, we examine the performance of MPI calls in the linear algebra mini-application AMG2013 when using MVAPICH and Spectrum-MPI. In doing so, we identify hidden performance losses in specific MPI functions. In the third case study, we illustrate the use of our Query Language in Hatchet's interactive visualization. In doing so, we show that our Query Language enables a simple and intuitive way to massively reduce profiling data.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages256-266
Number of pages11
ISBN (Electronic)9781665461245
DOIs
StatePublished - 2022
Externally publishedYes
Event18th IEEE International Conference on e-Science, eScience 2022 - Salt Lake City, United States
Duration: Oct 10 2022Oct 14 2022

Publication series

NameProceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022

Conference

Conference18th IEEE International Conference on e-Science, eScience 2022
Country/TerritoryUnited States
CitySalt Lake City
Period10/10/2210/14/22

Keywords

  • High Performance Computing
  • Message Passing
  • Performance Analysis
  • Query Language
  • Scientific Applications

ASJC Scopus subject areas

  • Library and Information Sciences
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Modeling and Simulation
  • Instrumentation

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