The High Performance LINPACK for Accelerator Introspection (HPL-AI) benchmark seeks to highlight the convergence of HPC and artificial intelligence (AI) workloads based on machine learning (ML) and deep learning (DL) by solving a system of linear equations using novel, mixed-precision algorithms that exploit modern hardware. While traditional HPC focuses on simulation runs for modeling phenomena in a variety of scientific disciplines, the mathematical models that drive these computations mostly require 64-bit accuracy. However, the ML/DL methods that fuel advances in AI can achieve the desired results at 32-bit or lower precisions. This lesser demand for working precision fueled a resurgence of interest in new hardware platforms that deliver a mix of unprecedented performance levels and energy savings to achieve the classification and recognition fidelity afforded by higher-accuracy formats on classic hardware.
HPL-AI strives to unite these two realms by connecting its solver formulation to the decades-old HPL framework of benchmarking supercomputers. A number of large-scale HPC installations—including some machines on the TOP500—have now been benchmarked with HPL-AI, starting with Oak Ridge National Laboratory’s Summit machine in 2019 and now including RIKEN’s Fugaku supercomputer, which achieved 2 exaFLOP/s in mixed-precision performance.
Find out more at https://icl.bitbucket.io/hpl-ai/