EROICA: Online Performance Troubleshooting for Large-scale Model Training

TL;DR

EROICA is an innovative online troubleshooting system designed for large-scale GPU clusters, enabling real-time diagnosis of hardware and software performance issues with high accuracy and minimal impact.

Contribution

It introduces the first comprehensive online troubleshooting approach for large-scale model training, combining profiling and differential observability across all machines.

Findings

Achieved 97.5% success rate in diagnosing performance issues.

Deployed on clusters with approximately 100,000 GPUs for over 1.5 years.

Effectively identifies both hardware and software problems in production environments.

Abstract

Troubleshooting performance problems of large model training (LMT) is immensely challenging, due to unprecedented scales of modern GPU clusters, the complexity of software-hardware interactions, and the data intensity of the training process. Existing troubleshooting approaches designed for traditional distributed systems or datacenter networks fall short and can hardly apply to real-world training systems. In this paper, we present EROICA, the first online troubleshooting system that provides both fine-grained observation based on profiling, and coverage of all machines in GPU clusters, to diagnose performance issues in production, including both hardware and software problems (or the mixture of both). EROICA effectively summarizes runtime behavior patterns of LMT function executions via online profiling, and leverages differential observability to localize the root cause with minimal production impact. EROICA has been deployed as a production service for large-scale GPU clusters of ~100,000 GPUs for 1.5 years. It has diagnosed a variety of difficult performance issues with 97.5% success.