Performance Study on CPU-based Machine Learning with PyTorch
Smeet Chheda, Anthony R. Curtis, Eva Siegmann, Barbara Chapman · 2023
Over the past decade we have seen a surge in research in Machine Learning. Deep neural networks represent a subclass of machine learning and are computationally intensive. Traditionally, GPUs have been leveraged to accelerate the training of such deep networks by taking advantage of parallelization and the many core architecture. As the datasets and models grow larger, scaling the training or inference task can help reduce the time to solution for research or production purposes. The Supercomputer Fugaku established state of the art results in multiple benchmarks in machine learning by scaling ARM based CPU technology. To that end, we study and present the performance of machine learning training and inference tasks on 64-bit ARM CPU architecture by exploiting its features namely the Scalable Vector Extensions (SVE) in the ARMv8-A.