- By:
- Tran, Brandon; Maiterth, Matthias ; Shin, Woong ; Sinclair, Matthew; Venkataraman, Shivaram
- Page Number:
- 145-160
- Book Title:
- Proceedings of the 40th ACM International Conference on Supercomputing (ICS '26)
- Publication Date:
- July 9, 2026
- Conference Name:
- 40th ACM International Conference on Supercomputing (ICS '26)
- Conference Location:
- Belfast, United Kingdom
- Conference Sponsor:
- ACM
- View DOI Listing:
- https://doi.org/10.1145/3797905.3800514
Abstract
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 Unfortunately, current GPU energy attribution techniques are either inaccurate, inflexible, or outdated.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%.
