Wattchmen: Watching the Wattchers -- High Fidelity, Flexible GPU Energy Modeling
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| Format: | Preprint |
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2026
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| _version_ | 1866911548261269504 |
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| author | Tran, Brandon Maiterth, Matthias Shin, Woong Sinclair, Matthew D. Venkataraman, Shivaram |
| author_facet | Tran, Brandon Maiterth, Matthias Shin, Woong Sinclair, Matthew D. Venkataraman, Shivaram |
| contents | Modern GPU-rich HPC systems are increasingly becoming energy-constrained. Thus, understanding an application's energy consumption becomes essential. Unfortunately, current GPU energy attribution techniques are either inaccurate, inflexible, or outdated. Therefore, we propose Wattchmen, a flexible methodology for measuring, attributing, and predicting GPU energy consumption. We construct a per-instruction energy model using a diverse set of microbenchmarks to systematically quantify the energy consumption of GPU instructions, enabling finer-grain prediction and energy consumption breakdowns for applications. Compared with the state-of-the-art systems like AccelWattch (32%) and Guser (25%), across 16 popular GPGPU, graph analytics, HPC, and ML workloads, Wattchmen reduces the mean absolute percent error (MAPE) to 14% on V100 GPUs. Furthermore, we show that Wattchmen provides similar MAPEs for water-cooled V100s (15%) and extends to later architectures, including air-cooled A100 (11%) and H100 (12%) GPUs. Finally, to further demonstrate Wattchmen's value, we apply it to applications such as Backprop and QMCPACK, where Wattchmen's insights enable energy reductions of up to 35%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_26435 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Wattchmen: Watching the Wattchers -- High Fidelity, Flexible GPU Energy Modeling Tran, Brandon Maiterth, Matthias Shin, Woong Sinclair, Matthew D. Venkataraman, Shivaram Hardware Architecture Distributed, Parallel, and Cluster Computing B.8.2; C.1.4; I.6.5 Modern GPU-rich HPC systems are increasingly becoming energy-constrained. Thus, understanding an application's energy consumption becomes essential. Unfortunately, current GPU energy attribution techniques are either inaccurate, inflexible, or outdated. Therefore, we propose Wattchmen, a flexible methodology for measuring, attributing, and predicting GPU energy consumption. We construct a per-instruction energy model using a diverse set of microbenchmarks to systematically quantify the energy consumption of GPU instructions, enabling finer-grain prediction and energy consumption breakdowns for applications. Compared with the state-of-the-art systems like AccelWattch (32%) and Guser (25%), across 16 popular GPGPU, graph analytics, HPC, and ML workloads, Wattchmen reduces the mean absolute percent error (MAPE) to 14% on V100 GPUs. Furthermore, we show that Wattchmen provides similar MAPEs for water-cooled V100s (15%) and extends to later architectures, including air-cooled A100 (11%) and H100 (12%) GPUs. Finally, to further demonstrate Wattchmen's value, we apply it to applications such as Backprop and QMCPACK, where Wattchmen's insights enable energy reductions of up to 35%. |
| title | Wattchmen: Watching the Wattchers -- High Fidelity, Flexible GPU Energy Modeling |
| topic | Hardware Architecture Distributed, Parallel, and Cluster Computing B.8.2; C.1.4; I.6.5 |
| url | https://arxiv.org/abs/2603.26435 |