Scrutinizing Variables for Checkpoint Using Automatic Differentiation
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arXiv
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| Format: | Preprint |
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2026
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| author | Huang, Xin Zhang, Weiping Meng, Shiman Xu, Wubiao Fu, Xiang Guo, Luanzheng Sato, Kento |
| author_facet | Huang, Xin Zhang, Weiping Meng, Shiman Xu, Wubiao Fu, Xiang Guo, Luanzheng Sato, Kento |
| contents | Checkpoint/Restart (C/R) saves the running state of the programs periodically, which consumes considerable system resources. We observe that not every piece of data is involved in the computation in typical HPC applications; such unused data should be excluded from checkpointing for better storage/compute efficiency. To find out, we propose a systematic approach that leverages automatic differentiation (AD) to scrutinize every element within variables (e.g., arrays) for checkpointing allowing us to identify critical/uncritical elements and eliminate uncritical elements from checkpointing. Specifically, we inspect every single element within a variable for checkpointing with an AD tool to determine whether the element has an impact on the application output or not. We empirically validate our approach with eight benchmarks from the NAS Parallel Benchmark (NPB) suite. We successfully visualize critical/uncritical elements/regions within a variable with respect to its impact (yes or no) on the application output. We find patterns/distributions of critical/uncritical elements/regions quite interesting and follow the physical formulation/logic of the algorithm.The evaluation on NPB benchmarks shows that our approach saves storage for checkpointing by up to 20%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16010 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scrutinizing Variables for Checkpoint Using Automatic Differentiation Huang, Xin Zhang, Weiping Meng, Shiman Xu, Wubiao Fu, Xiang Guo, Luanzheng Sato, Kento Distributed, Parallel, and Cluster Computing Checkpoint/Restart (C/R) saves the running state of the programs periodically, which consumes considerable system resources. We observe that not every piece of data is involved in the computation in typical HPC applications; such unused data should be excluded from checkpointing for better storage/compute efficiency. To find out, we propose a systematic approach that leverages automatic differentiation (AD) to scrutinize every element within variables (e.g., arrays) for checkpointing allowing us to identify critical/uncritical elements and eliminate uncritical elements from checkpointing. Specifically, we inspect every single element within a variable for checkpointing with an AD tool to determine whether the element has an impact on the application output or not. We empirically validate our approach with eight benchmarks from the NAS Parallel Benchmark (NPB) suite. We successfully visualize critical/uncritical elements/regions within a variable with respect to its impact (yes or no) on the application output. We find patterns/distributions of critical/uncritical elements/regions quite interesting and follow the physical formulation/logic of the algorithm.The evaluation on NPB benchmarks shows that our approach saves storage for checkpointing by up to 20%. |
| title | Scrutinizing Variables for Checkpoint Using Automatic Differentiation |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2602.16010 |