MLPerf: A Benchmark for Machine Learning

Machine learning applications are rapidly expanding into scientific domains and challenging the hallmarks of traditional HPC workloads. We present MLPerf, a community-driven system performance benchmark which spans a range of machine learning tasks. The speakers at this BoF are experts in the fields of HPC, scientific computing, machine learning, and computer architecture, representing academia, government research organizations, and private industry. In this session, we will cover v3.0 of the MLPerf HPC benchmark suite: we will discuss rule changes and present recurring and newly added benchmarks. One of the most important goals in the near future is to simplify benchmark submissions and improve the longevity of benchmark results. In this context, we are looking forward to discussing the state of MLPerf HPC with the broader HPC community.