Automated Programmatic Performance Analysis of Parallel Programs Article Swipe
Onur Cankur
,
Aditya Tomar
,
Daniel Nichols
,
Connor Scully-Allison
,
Katherine E. Isaacs
,
Abhinav Bhatelé
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.13150
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.13150
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.13150
- https://arxiv.org/pdf/2401.13150
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391244561
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4391244561Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.13150Digital Object Identifier
- Title
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Automated Programmatic Performance Analysis of Parallel ProgramsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-23Full publication date if available
- Authors
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Onur Cankur, Aditya Tomar, Daniel Nichols, Connor Scully-Allison, Katherine E. Isaacs, Abhinav BhateléList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.13150Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2401.13150Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2401.13150Direct OA link when available
- Concepts
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Python (programming language), Computer science, Scalability, Data analysis, Distributed computing, Operating system, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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