When Log Parsing Removes What Matters: Detecting Failures in Distributed GPU Workloads
Large AI models increasingly rely on distributed GPU execution, yet it remains unclear whether established failure analysis methods transfer effectively to this setting. We introduce GF-Bench, a PyTorch-based framework and dataset for generating diverse failures in distributed GPU workloads.
This paper introduces GF-Bench, a framework and dataset for studying failure detection in distributed GPU workloads, highlighting challenges in log parsing for AI system reliability.
What happened
Researchers developed GF-Bench to evaluate failure detection in distributed GPU systems. They tested sequence-based methods and LLMs on parsed vs. raw logs, finding that while raw logs improve accuracy, they require significantly more computational resources.
Why it matters
Effective failure detection is critical for maintaining reliability in large-scale AI systems. The study informs trade-offs between operational efficiency and diagnostic accuracy in distributed GPU environments.
- Log parsing reduces costs but may sacrifice diagnostic accuracy
- LLMs show promise for raw log analysis but require careful resource management
- Distributed GPU systems require specialized failure detection approaches
- GF-Bench provides a standardized platform for evaluating failure analysis methods
- Operational efficiency and diagnostic precision are conflicting objectives in system monitoring
The bigger picture
The findings influence design decisions for AI system monitoring tools, emphasizing the need to balance parsing efficiency with contextual information retention for accurate failure detection.
GF-Bench advances research into reliable AI system monitoring by exposing the trade-offs between log parsing efficiency and failure detection accuracy in distributed GPU environments.
Technical details
- PyTorch-based framework for GPU workload simulation
- CUDA out-of-memory fail-stop failures
- Persistent straggler fail-slow failures
- LogBERT, DeepLog, LogAnomaly, and LogPrompt evaluation methods
- Token usage and cost metrics for log analysis
FAQ
What is GF-Bench?
GF-Bench is a PyTorch-based framework and dataset for generating and studying failures in distributed GPU workloads, enabling research on failure detection methods.
What are the main findings?
Parsed logs reduce operational costs but hinder accurate failure detection, while raw log analysis with LLMs achieves higher accuracy at significantly higher computational costs.
What types of failures does GF-Bench support?
It targets CUDA out-of-memory fail-stop failures and persistent straggler fail-slow failures in multi-GPU environments.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv cs.LG on Oct 14, 2026.
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