Geodesic
Comparison and Analysis of Software and Hardware Energy Measurement Methods for a CPU+GPU System and Selected Parallel Applications
Computer Science and Information Systems, Tome 22 (2025) no. 2

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In this paper authors extend upon their previous research on powercapped 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.
Keywords: high performance computing, performance-energy optimization, energy measurements, measurement accuracy, parallel benchmarks 
Grzegorz Koszczał; Mariusz Matuszek; Paweł Czarnul. Comparison and Analysis of Software and Hardware Energy Measurement Methods for a CPU+GPU System and Selected Parallel Applications. Computer Science and Information Systems, Tome 22 (2025) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2025_22_2_a6/
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     title = {Comparison and {Analysis} of {Software} and {Hardware} {Energy} {Measurement} {Methods} for a {CPU+GPU} {System} and {Selected} {Parallel} {Applications}},
     journal = {Computer Science and Information Systems},
     year = {2025},
     volume = {22},
     number = {2},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2025_22_2_a6/}
}
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