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Improving Parallel I/O Performance with Data Layout Awareness

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5 Author(s)
Yong Chen ; Comput. Sci. & Math. Div., Oak Ridge Nat. Lab., Oak Ridge, TN, USA ; Xian-He Sun ; Thakur, R. ; Huaiming Song
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Parallel applications can benefit greatly from massive computational capability, but their performance suffers from large latency of I/O accesses. The poor I/O performance has been attributed as a critical cause of the low sustained performance of parallel computing systems. In this study, we propose a data layout-aware optimization strategy to promote a better integration of the parallel I/O middleware and parallel file systems, two major components of the current parallel I/O systems, and to improve the data access performance. We explore the layout-aware optimization in both independent I/O and collective I/O, two primary forms of I/O in parallel applications. We illustrate that the layout-aware I/O optimization could improve the performance of current parallel I/O strategy effectively. The experimental results verify that the proposed strategy could improve parallel I/O performance by nearly 40% on average. The proposed layout-aware parallel I/O has a promising potential in improving the I/O performance of parallel systems.

Published in:

Cluster Computing (CLUSTER), 2010 IEEE International Conference on

Date of Conference:

20-24 Sept. 2010