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Novel artificial intelligence (AI) technology has expedited variousscientific research, e.g., cosmology, physics and bioinformatics, inevitablybecoming a significant category of workload on high performance computing (HPC)systems. Existing AI benchmarks tend to customize well-recognized AIapplications, so as to evaluate the AI performance of HPC systems underpredefined problem size, in terms of datasets and AI models. Due to lack ofscalability on the problem size, static AI benchmarks might be under competentto help understand the performance trend of evolving AI applications on HPCsystems, in particular, the scientific AI applications on large-scale systems.  In this paper, we propose a scalable evaluation methodology (SAIH) foranalyzing the AI performance trend of HPC systems with scaling the problemsizes of customized AI applications. To enable scalability, SAIH builds a setof novel mechanisms for augmenting problem sizes. As the data and modelconstantly scale, we can investigate the trend and range of AI performance onHPC systems, and further diagnose system bottlenecks. To verify ourmethodology, we augment a cosmological AI application to evaluate a real HPCsystem equipped with GPUs as a case study of SAIH.

SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems