Comparative evaluation of deep learning workloads for leadership-class systems

Conference · Fri Oct 01 04:00:00 EDT 2021

DOI:https://doi.org/10.1016/j.tbench.2021.100005· OSTI ID:1838972

Yin, Junqi ^[1]; Tsaris, Aristeidis (aris) ^[1]; Dash, Sajal ^[1]; Miller, Ross ^[1]; Wang, Feiyi ^[1]; Shankar, Mallikarjun (Arjun) ^[1]

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1. ORNL

Deep learning (DL) workloads and their performance at scale are becoming important factors to consider as we design, develop and deploy next-generation high-performance computing systems. Since DL applications rely heavily on DL frameworks and underlying compute (CPU/GPU) stacks, it is essential to gain a holistic understanding from compute kernels, models, and frameworks of popular DL stacks, and to assess their impact on science-driven, mission-critical applications. At Oak Ridge Leadership Computing Facility (OLCF), we employ a set of micro and macro DL benchmarks established through the Collaboration of Oak Ridge, Argonne, and Livermore (CORAL) to evaluate the AI readiness of our next-generation supercomputers. In this paper, we present our early observations and performance benchmark comparisons between the Nvidia V100 based Summit system with its CUDA stack and an AMD MI100 based testbed system with its ROCm stack. We take a layered perspective on DL benchmarking and point to opportunities for future optimizations in the technologies that we consider.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1838972
• Country of Publication: United States
• Language: English