Dynamic Detection of Inefficient Data Mapping Patterns in Heterogeneous OpenMP Applications
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912831971000320 |
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| author | Marzen, Luke Shim, Junhyung Jannesari, Ali |
| author_facet | Marzen, Luke Shim, Junhyung Jannesari, Ali |
| contents | With the growing prevalence of heterogeneous computing, CPUs are increasingly being paired with accelerators to achieve new levels of performance and energy efficiency. However, data movement between devices remains a significant bottleneck, complicating application development. Existing performance tools require considerable programmer intervention to diagnose and locate data transfer inefficiencies. To address this, we propose dynamic analysis techniques to detect and profile inefficient data transfer and allocation patterns in heterogeneous applications. We implemented these techniques into OMPDataPerf, which provides detailed traces of problematic data mappings, source code attribution, and assessments of optimization potential in heterogeneous OpenMP applications. OMPDataPerf uses the OpenMP Tools Interface (OMPT) and incurs only a 5 % geometric-mean runtime overhead. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_12713 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamic Detection of Inefficient Data Mapping Patterns in Heterogeneous OpenMP Applications Marzen, Luke Shim, Junhyung Jannesari, Ali Distributed, Parallel, and Cluster Computing With the growing prevalence of heterogeneous computing, CPUs are increasingly being paired with accelerators to achieve new levels of performance and energy efficiency. However, data movement between devices remains a significant bottleneck, complicating application development. Existing performance tools require considerable programmer intervention to diagnose and locate data transfer inefficiencies. To address this, we propose dynamic analysis techniques to detect and profile inefficient data transfer and allocation patterns in heterogeneous applications. We implemented these techniques into OMPDataPerf, which provides detailed traces of problematic data mappings, source code attribution, and assessments of optimization potential in heterogeneous OpenMP applications. OMPDataPerf uses the OpenMP Tools Interface (OMPT) and incurs only a 5 % geometric-mean runtime overhead. |
| title | Dynamic Detection of Inefficient Data Mapping Patterns in Heterogeneous OpenMP Applications |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2601.12713 |