The 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 require, for the most part, 64-bit accuracy. On the other hand, the ML/DL methods that fuel advances in AI can achieve the desired results at 32-bit or even lower floating-point precisions. This lesser demand for accuracy 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.
HPL-AI strives to unite these two realms by connecting its solver formulation to the decades-old HPL framework of benchmarking supercomputing installations. So far, Oak Ridge National Laboratory’s Summit is the only machine to be benchmarked at scale with HPL-AI, and it achieved 445 PFLOP/s in mixed precision. This is nearly triple the 148 PFLOP/s that Summit achieved on the standard (double-precision) HPL benchmark used for the TOP500.
Find out more at https://icl.bitbucket.io/hpl-ai/