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Comparison and analysis of software and hardware energy measurement methods for a CPU+GPU system and selected parallel applications
Abstract
In this paper authors extend upon their previous research on power-capped optimization of performance-energy metrics of deep neural networks training workloads. A professional power meter Yokogawa WT-310E is used, as well as Intel RAPL and Nvidia NVML interfaces, to examine power consumption of a much more comprehensive set of multi-GPU and multi-CPU workloads, including: selected kernels from NAS Parallel Benchmarks for CPUs and GPUs as well as Horovod-Python Xception deep neural network training using several GPUs. A comparison and discussion of results obtained by both power measurement methods has been performed using 2 systems, one with 2 Intel Xeon CPUs and 8 Nvidia Quadro RTX 6000 GPUs and the second 2 Intel Xeon CPUs and 4 Nvidia Quadro RTX 5000 GPUs. We compared power consumption between hardware and software interfaces for CPU, GPU and mixed CPU+GPU workload configurations, using 1-40 threads for the CPUs and 1-8 GPUs.
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.2298/CSIS240722023K
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Computer Science and Information Systems
no. 22,
pages 563 - 590,
ISSN: 1820-0214 - Language:
- English
- Publication year:
- 2025
- Bibliographic description:
- Koszczał G., Matuszek M., Czarnul P.: Comparison and analysis of software and hardware energy measurement methods for a CPU+GPU system and selected parallel applications// Computer Science and Information Systems -Vol. 22,iss. 2 (2025), s.563-590
- DOI:
- Digital Object Identifier (open in new tab) 10.2298/csis240722023k
- Sources of funding:
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- Cloud Artificial Intelligence Service Engineering (CAISE)
- CERCIRAS COST Action CA19135
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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