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Automatic parallel I/O performance optimization using genetic algorithms

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4 Author(s)
Y. Chen ; Comput. Sci. Dept., IBM Almaden Res. Center, San Jose, CA, USA ; M. Winslett ; Y. Cho ; S. Kuo

The complexity of parallel I/O systems imposes significant challenge in managing and utilizing the available system resources to meet application performance, portability and usability goals. We believe that a parallel I/O system that automatically selects efficient I/O plans for user applications is a solution to this problem. We present such an automatic performance optimization approach for scientific applications performing collective I/O requests on multidimensional arrays. The approach is based on a high level description of the target workload and execution environment characteristics, and applies genetic algorithms to select high quality I/O plans. We have validated this approach in the Panda, parallel I/O library. Our performance evaluations on the IBM SP show that this approach can select high quality I/O plans under a variety of system conditions with a low overhead, and the genetic algorithm-selected I/O plans are in general better than the default plans used in Panda

Published in:

High Performance Distributed Computing, 1998. Proceedings. The Seventh International Symposium on

Date of Conference:

28-31 Jul 1998