Fine-grained Automated Failure Management for Extreme-Scale GPU Accelerated Systems
Automated Failure Management for Extreme-Scale GPU Systems
This paper introduces an automated failure management system designed to minimize Mean Time to Repair (MTTR) in extreme-scale GPU accelerated systems like the Aurora supercomputer. By leveraging a centralized meta-database for event history analysis, fine-grained multi-strike repair policies, and an automated recovery framework, the system significantly reduces downtime and maintenance costs, achieving up to an 84X improvement over manual methods.
Executive Impact & Key Findings
Our analysis reveals critical improvements in system reliability and operational efficiency for extreme-scale GPU accelerated systems, demonstrating significant reductions in downtime and maintenance costs.
84X
MTTR Reduction
98.42%
System Availability
>30
Human Effort Years Saved
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Node Error Trigger
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Quarantine & Bundle Collect
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Process Strike Policy
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Execute Micro-actions
→
Online/RMA Decision
10,624
Compute Nodes
63,744
PVC GPUs
>1
TB Telemetry/Day
84X
MTTR Reduction vs. Manual
| Feature | Automated System | Manual Approach |
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| MTTR |
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| Decision Basis |
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| Error Handling |
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| Correlated Events |
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| Cost Efficiency |
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Aurora Supercomputer Deployment
Description: The system was deployed on the Aurora supercomputer at Argonne National Laboratory during its acceptance testing phase.
Challenge: During bring-up, Aurora experienced fluctuating error rates and bursts of correlated failures, often overwhelming maintenance teams and impacting system availability. Manual repair methods were slow and costly.
Solution: Implemented a centralized meta-database for real-time event correlation, fine-grained multi-strike repair policies driven by statistical properties, and an automated execution framework for diagnostics and repair.
Result: Achieved an 84X reduction in MTTR for compute blades compared to manual methods, significantly increased system availability (98.42% average), and saved an estimated 30 human effort years in triage.
98.42%
Achieved System Availability
>30
Human Years Saved
>100
Policy Entries
<0.23
Projected MTBF (hours)
Calculate Your Potential ROI
Estimate the significant savings and efficiency gains your enterprise could achieve with AI-powered failure management.
Annual Savings
$0
Annual Hours Reclaimed
0
Your Implementation Roadmap
A phased approach to integrate advanced AI-driven failure management into your existing HPC and GPU accelerated infrastructure.
Phase 1: Enhanced Telemetry & ML Integration
Integrate additional telemetry (performance metrics, application logs) and develop advanced machine learning models for resource optimization and failure prediction, moving towards proactive avoidance.
Phase 2: Open-Source Framework Development
Redesign the infrastructure to be more generalized, supporting diverse schedulers (Kubernetes, SLURM) and alternative information sources. Prepare for open-sourcing the service to foster community contributions.
Phase 3: Broadened Failure Handling
Extend automated failure management to comprehensively cover software-related errors and communication hangs, beyond current hardware-centric focus. Implement robust software error classification and recovery mechanisms.
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