Abstract
Many scientific applications use parallel I/O to meet the low latency and high bandwidth I/O requirement. Among many available parallel I/O operations, collective I/O is one of the most popular methods when the storage layouts and access patterns of data do not match. The implementation of collective I/O typically involves disk I/O operations followed by interprocessor communications. Also, in many I/O-intensive applications, parallel I/O operations are usually followed by parallel computations. This paper presents a comparative study of different overlap strategies in parallel applications. We have experimented with four different overlap strategies 1) Overlapping I/O and communication; 2) Overlapping I/O and computation; 3) Overlapping computation and communication; and 4) Overlapping I/O, communication, and computation. All experiments have been conducted on a Linux Cluster and the performance results obtained are very encouraging. On an average, we have enhanced the performance of a generic collective read call by 38%, the MxM benchmark by 26%, and the FFT benchmark by 34%.
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