The effectiveness and efficiency of machine learning methodologies are crucial, especially with respect to the quality of results and computational cost. This paper discusses different model optimization techniques, providing a comprehensive analysis of key performance indicators. Several parallelization strategies for image recognition, adapted to different hardware and software configurations, including distributed data parallelism and distributed hardware processing, are analyzed. Selected optimization strategies are studied in detail, highlighting the related challenges and advantages of their implementation. Furthermore, the impact of different performance improvement techniques (DPO, LoRA, QLoRA, and QAT) on the tuning process of large language models is investigated. Experimental results illustrate how the nature of the task affects the iteration time in a multiprocessor environment, VRAM utilization, and overall memory transfers. Test scenarios are evaluated on the modern NVIDIA H100 GPU architecture.
@article{arxiv.2505.22905,
title = {Profiling and optimization of multi-card GPU machine learning jobs},
author = {Marcin Lawenda and Kyrylo Khloponin and Krzesimir Samborski and Łukasz Szustak},
journal= {arXiv preprint arXiv:2505.22905},
year = {2025}
}
Comments
27 pages, 28 figures. arXiv admin note: substantial text overlap with arXiv:2503.15252