AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications

Baolin Li, Rohin Arora, Siddharth Samsi, Tirthak Patel, William Arcand, David Bestor, Chansup Byun, Rohan Basu Roy, Bill Bergeron, John T. Holodnak, Michael Houle, Matthew Hubbell, Michael Jones, Jeremy Kepner, Anna Klein, Peter Michaleas, Joseph McDonald, Lauren Milechin, Julie Mullen, Andrew Prout et al. (10 additional authors not shown)

Abstract: Production high-performance computing (HPC) systems are adopting and integrating GPUs into their design to accommodate artificial intelligence (AI), machine learning, and data visualization workloads. To aid with the design and operations of new and existing GPU-based large-scale systems, we provide a detailed characterization of system operations, job characteristics, user behavior, and trends on a contemporary GPU-accelerated production HPC system. Our insights indicate that the pre-mature phases in modern AI workflow take up significant GPU hours while underutilizing GPUs, which opens up the opportunity for a multi-tier system. Finally, we provide various potential recommendations and areas for future investment for system architects, operators, and users.