Comparison and analysis of software and hardware energy measurement methods for a CPU+GPU system and selected parallel applications

• Published in: Computer Science and Information Systems Vol. 22; no. 2; pp. 563 - 590
• Main Authors: Koszczał, Grzegorz, Matuszek, Mariusz, Czarnul, Paweł
• Format: Journal Article
• Language: English
• Published: 01.04.2025
• ISSN: 1820-0214; 2406-1018
• DOI: 10.2298/CSIS240722023K

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.