Publication

Machine Learning Models for GPU Error Prediction in a Large Scale HPC System

Citation

B. Nie, J. Xue, S. Gupta, T. Patel, C. Engelmann, E. Smirni, and D. Tiwari. Machine Learning Models for GPU Error Prediction in a Large Scale HPC System. In Proceedings of the 48th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2018, Luxembourg City, Luxembourg, June 25-28, 2018.

Abstract

GPUs are widely deployed on large-scale HPC systems to provide powerful computational capability for scientific applications from various domains. As those applications are normally long-running, investigating the characteristics of GPU errors becomes imperative for reliability. In this paper, we first study the system conditions that trigger GPU errors using sixmonth trace data collected from a large-scale, operational HPC system. Then, we use machine learning to predict the occurrence of GPU errors, by taking advantage of temporal and spatial dependencies of the trace data. The resulting machine learning prediction framework is robust and accurate under different workloads.

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