The content describes a process to efficiently identify and mitigate the impact of slow-performing nodes in a large supercomputer cluster. Key highlights:
The author developed a set of proxy applications (both MPI and OpenMP-based) that can quickly assess node performance, as an alternative to the time-consuming High-Performance Linpack (HPL) benchmark.
The performance data from the proxy applications was analyzed using techniques like linear regression, Mahalanobis distance, and machine learning (Random Forests, neural networks) to identify outlier nodes that are significantly underperforming.
The author found that CPU speed had the largest variation in performance across nodes, indicating that efforts should focus on reducing CPU performance differences rather than memory or interconnect.
Strategies for mitigating the impact of slow nodes were discussed, including trimming, replacement, prioritized scheduling, and customized node preference lists.
The process was able to identify 12 out of 33 slow nodes using simple regression, at least 20 out of 33 using Mahalanobis distance, and 18 out of 33 using neural network regression, demonstrating the effectiveness of the approach.
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by Phil Romero at arxiv.org 04-17-2024
https://arxiv.org/pdf/2404.10617.pdfDeeper Inquiries