Enabling Call Path Querying in Hatchet to Identify Performance Bottlenecks in Scientific Applications Article Swipe
Ian Lumsden
,
Jakob Luettgau
,
Vanessa Lama
,
Connor Scully-Allison
,
Stephanie Brink
,
Katherine E. Isaacs
,
Olga Pearce
,
Michela Taufer
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/escience55777.2022.00039
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/escience55777.2022.00039
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/escience55777.2022.00039
- OA Status
- green
- Cited By
- 4
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307080439
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307080439Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/escience55777.2022.00039Digital Object Identifier
- Title
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Enabling Call Path Querying in Hatchet to Identify Performance Bottlenecks in Scientific ApplicationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-10-01Full publication date if available
- Authors
-
Ian Lumsden, Jakob Luettgau, Vanessa Lama, Connor Scully-Allison, Stephanie Brink, Katherine E. Isaacs, Olga Pearce, Michela TauferList of authors in order
- Landing page
-
https://doi.org/10.1109/escience55777.2022.00039Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://zenodo.org/record/7158671Direct OA link when available
- Concepts
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Computer science, Query language, Profiling (computer programming), Visualization, Performance tuning, Path (computing), Graph, Information retrieval, Theoretical computer science, Programming language, Data miningTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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