![]() The methodology behind HPL-AI is outlined in a paper published at SC 2018 by Azzam Haidar, Dongarra and his team. “Just as HPL allows benchmarking of double-precision capabilities, this new approach based on HPL allows benchmarking of mixed-precision capabilities of supercomputers at scale.” Mixed-precision techniques have become increasingly important to improve the computing efficiency of supercomputers, both for traditional simulations with iterative refinement techniques as well as for AI applications,” Dongarra said. Our test implementing HPL-AI on the Summit supercomputer affirms the feasibility of HPL-AI measurements at scale to gauge mixed-precision computing performance and complement existing HPL benchmarks. To account for the AI techniques that represent the new era of supercomputing, a new approach to benchmarking based on the HPL standard - called HPL-AI - incorporates the mixed-precision calculations widely used to train AI models. And most AI models use mixed-precision math - a fundamentally different technique that enables researchers to improve the computational efficiency and access the untapped performance potential in modern supercomputers. While HPL continues to be a trusted benchmark to measure the performance of TOP500 systems for HPC applications, modern supercomputers are now being used for AI applications, not just simulations. The benchmark estimates the performance of a supercomputer to run HPC applications, like simulations, using double-precision math. Since its introduction roughly three decades ago by high-performance computing luminary Jack Dongarra, the Linpack benchmark has stood the test of time, providing a consistent measurement of supercomputing muscle. The High-Performance Linpack benchmark, or HPL, has long been a yardstick of performance for supercomputers and the basis for the biannual TOP500 ranking. That compares with the system’s official performance of 148 petaflops announced in the new TOP500 list of the world’s fastest supercomputers. Using HPL-AI, a new approach to benchmarking AI supercomputers, Oak Ridge National Laboratory’s Summit system has achieved unprecedented performance levels of 445 petaflops or nearly half an exaflops.
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