Year 2025

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

  Publication

  • G. Koszczał
  • M. Matuszek
  • P. Czarnul

  - Computer Science and Information Systems - Year 2025

  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...

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Year 2024

• Performance and Energy Aware Training of a Deep Neural Network in a Multi-GPU Environment with Power Capping

  Publication

  • G. Koszczał
  • J. Dobrosolski
  • M. Matuszek
  • P. Czarnul

  - Year 2024

  In this paper we demonstrate that it is possible to obtain considerable improvement of performance and energy aware metrics for training of deep neural networks using a modern parallel multi-GPU system, by enforcing selected, non-default power caps on the GPUs. We measure the power and energy consumption of the whole node using a professional, certified hardware power meter. For a high performance workstation with 8 GPUs, we were...

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