Pipit: Scripting the analysis of parallel execution traces
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911876239065088 |
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| author | Bhatele, Abhinav Dhakal, Rakrish Movsesyan, Alexander Ranjan, Aditya K. Cankur, Onur |
| author_facet | Bhatele, Abhinav Dhakal, Rakrish Movsesyan, Alexander Ranjan, Aditya K. Cankur, Onur |
| contents | Performance analysis is a critical step in the oft-repeated, iterative process of performance tuning of parallel programs. Per-process, per-thread traces (detailed logs of events with timestamps) enable in-depth analysis of parallel program execution to identify different kinds of performance issues. Often times, trace collection tools provide a graphical tool to analyze the trace output. However, these GUI-based tools only support specific file formats, are challenging to scale to large trace sizes, limit data exploration to the implemented graphical views, and do not support automated comparisons of two or more datasets. In this paper, we present a programmatic approach to analyzing parallel execution traces by leveraging pandas, a powerful Python-based data analysis library. We have developed a Python library, Pipit, on top of pandas that can read traces in different file formats (OTF2, HPCToolkit, Projections, Nsight Systems, etc.) and provides a uniform data structure in the form of a pandas DataFrame. Pipit provides operations to aggregate, filter, and transform the events in a trace to present the data in different ways. We also provide several functions to quickly and easily identify performance issues in parallel executions. More importantly, the API is easily extensible to support custom analyses by different end users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_11177 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Pipit: Scripting the analysis of parallel execution traces Bhatele, Abhinav Dhakal, Rakrish Movsesyan, Alexander Ranjan, Aditya K. Cankur, Onur Distributed, Parallel, and Cluster Computing Performance Performance analysis is a critical step in the oft-repeated, iterative process of performance tuning of parallel programs. Per-process, per-thread traces (detailed logs of events with timestamps) enable in-depth analysis of parallel program execution to identify different kinds of performance issues. Often times, trace collection tools provide a graphical tool to analyze the trace output. However, these GUI-based tools only support specific file formats, are challenging to scale to large trace sizes, limit data exploration to the implemented graphical views, and do not support automated comparisons of two or more datasets. In this paper, we present a programmatic approach to analyzing parallel execution traces by leveraging pandas, a powerful Python-based data analysis library. We have developed a Python library, Pipit, on top of pandas that can read traces in different file formats (OTF2, HPCToolkit, Projections, Nsight Systems, etc.) and provides a uniform data structure in the form of a pandas DataFrame. Pipit provides operations to aggregate, filter, and transform the events in a trace to present the data in different ways. We also provide several functions to quickly and easily identify performance issues in parallel executions. More importantly, the API is easily extensible to support custom analyses by different end users. |
| title | Pipit: Scripting the analysis of parallel execution traces |
| topic | Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2306.11177 |