Abstract

Users' limited understanding of the storage system architecture prevents them from fully utilizing the parallel I/O capability of the storage system, leading to a negative impact on the overall performance of supercomputers. Therefore, exploring effective strategies for utilizing parallel I/O capabilities is imperative. In this regard, we conduct an analysis of the workload on two production supercomputers' Object Storage Targets (OSTs) and study the potential inefficient I/O patterns for high performance computing jobs. Our research findings indicate that under the traditional stripe settings that most supercomputers use to ensure stability, the real-time load on OSTs is severely unbalanced. This imbalance results in I/O requests that fail to fully utilize the available OSTs. To tackle this issue, we propose a job-aware optimization approach, which includes static and dynamic file striping. Static file striping optimizes all user jobs, whereas dynamic file striping employs clustering of job names and job paths to extract similarities among jobs and predict partially stripe-optimizable jobs for users. Additionally, a stripe recovery mechanism is employed to mitigate the negative impact of stripe misconfigurations. This approach appropriately adjusts the file stripe layout based on the job's I/O pattern, allowing for better mobilization of underutilized OSTs to enhance parallel I/O capabilities. Through experimental verification, the number of OSTs that jobs can use has been increased, effectively improving the parallel I/O performance of the job without significantly affecting operational stability.