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

Feb 1, 2022ยท
Baolin Li
Baolin Li
,
Rohin Arora
,
Siddharth Samsi
,
Tirthak Patel
,
Rohan Basu Roy
,
Vijay Gadepally
,
Devesh Tiwari
,
Et Al.
ยท 0 min read
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.
Type
Publication
In Proceedings of the 28th IEEE International Symposium on High Performance Computer Architecture (HPCA)