HPC AI500 V3.0: A scalable HPC AI benchmarking framework

Abstract

In recent years, the convergence of High Performance Computing (HPC) and artificial intelligence (AI) makes the community desperately need a benchmark to guide the design of next-generation scalable HPC AI systems. The success of the HPL benchmarks and the affiliated TOP500 ranking indicates that scalability is the fundamental requirement to evaluate HPC systems. However, being scalable in terms of these emerging AI workloads like deep learning (DL) raises nontrivial challenges. This paper formally and systematically analyzes the factor that limits scalability in DL workloads and presents HPC AI500 v3.0, a scalable HPC AI benchmarking framework. The HPC AI500 V3.0 methodology is inspired by bagging, which utilizes the collective wisdom of an ensemble of base models and enables the benchmarks to be adaptively scalable to different scales of HPC systems. We implement HPC AI500 V3.0 in a highly customizable manner, maintaining the space of various optimization from both system and algorithm levels. By reusing the representative workloads in HPC AI500 V2.0, we evaluate HPC AI500 V3.0 on typical HPC systems, and the results show it has near-linear scalability. Furthermore, based on the customizable design, we present a case study to perform a trade-off between AI model quality and its training speed. The source code of HPC AI500 V3.0 is publicly available from the HPC AI500 project homepage https://www.benchcouncil.org/aibench/hpcai500/.