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Exploring the Impacts of Multiple I/O Metrics in Identifying I/O Bottlenecks

DescriptionHPC systems, driven by the rise of workloads with significant data requirements, face challenges in I/O performance. To address this, a thorough I/O analysis is crucial to identify potential bottlenecks. However, the multitude of metrics makes it difficult to pinpoint the causes of low I/O performance. In this work, we analyze three scientific workloads using three widely accepted I/O metrics. We demonstrate that different metrics uncover different I/O bottlenecks, highlighting the importance of considering multiple metrics for comprehensive I/O analysis.

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