The Role of GPUs in Accelerating Machine Learning Workloads

Authors

  • Rajeev Reddy Chevuri Campbellsville University, USA Author

DOI:

https://doi.org/10.32628/CSEIT251127424

Keywords:

GPU acceleration, neural networks, parallel computing, model training, inference optimization

Abstract

This article presents a comprehensive overview of Graphics Processing Units (GPUs) and their transformative role in accelerating machine learning workloads. Starting with an explanation of the fundamental architectural differences between GPUs and CPUs, the article explores how the parallel processing capabilities of GPUs enable dramatic improvements in training deep learning models. The discussion covers GPU applications across convolutional neural networks, transformer architectures, and multi-GPU training strategies. Beyond training, the article examines GPU acceleration in inference, scientific computing, data preprocessing, and emerging application domains. Cost-effective deployment strategies are also addressed, including cloud versus on-premises considerations, container orchestration, dynamic resource allocation, and computational optimization techniques. Throughout, the article highlights how GPUs have fundamentally altered what is computationally feasible in artificial intelligence, enabling complex models and applications that would otherwise remain theoretical.

Downloads

Download data is not yet available.

References

Samyam Rajbhandari et al., "ZeRO: Memory Optimizations Toward Training Trillion Parameter Models," arxiv, 2020. [Online]. Available: https://arxiv.org/pdf/1910.02054

Tom B. Brown et al., "Language Models are Few-Shot Learners," arXiv, 2020. [Online]. Available: https://arxiv.org/pdf/2005.14165

Zhe Jia et al., "Dissecting the Graphcore IPU Architecture via Microbenchmarking," arXiv, 2019. [Online]. Available: https://arxiv.org/pdf/1912.03413

Chongxuan Li et al., "Graphical Generative Adversarial Networks," arXiv, 2018. [Online]. Available: https://arxiv.org/pdf/1804.03429

Jacob Devlin et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," Proceedings of NAACL-HLT 2019, pages 4171–4186, 2019. [Online]. Available: https://aclanthology.org/N19-1423.pdf

NVIDIA Docs Hub "Train With Mixed Precision," NVIDIA Deep Learning Performance. [Online]. Available: https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html

Vijay Janapa Reddi et al., "MLPerf Inference Benchmark," arxiv, 2020. [Online]. Available: https://arxiv.org/pdf/1911.02549

John E. Stone et al., "OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems," Computing in Science & Engineering 12(3):66-72, 2010. [Online]. Available: https://www.researchgate.net/publication/47636665_OpenCL_A_Parallel_Programming_Standard_for_Heterogeneous_Computing_Systems

Manav Madan et al.,"Comparison of Benchmarks for Machine Learning Cloud Infrastructures," The Twelfth International Conference on Cloud Computing, GRIDs, and Virtualization, 2021. [Online]. Available: https://personales.upv.es/thinkmind/dl/conferences/cloudcomputing/cloud_computing_2021/cloud_computing_2021_3_10_20011.pdf

Tal Ben Nun and Torsten Hoefler "Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis," arxiv, 2018. [Online]. Available: https://arxiv.org/pdf/1802.09941

Downloads

Published

28-03-2025

Issue

Section

Research Articles