MLPerf Storage Benchmark Results ‘23: Top Performers
Evaluating Data Processing Efficiency for Machine Learning Workloads
Introduction: Machine learning (ML) workloads require massive amounts of data processing. The efficiency of data processing plays a significant role in the overall performance of ML models. MLPerf, an open benchmarking initiative, provides a standardized way to evaluate the performance of ML models and the systems that run them. In this article, we focus on the MLPerf Storage Benchmark, which measures the data processing efficiency of various storage systems for ML workloads.