Paper Info
Title
Automatic Parallel I/O Performance Optimization Using Genetic Algorithms
Abstract
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. In this paper, 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.
Year
DOI
Venue
1998
10.1109/HPDC.1998.709968
HPDC
Keywords
Field
DocType
genetic algorithms,available system resource,o performance optimization,o library,high level description,o plan,application performance,high quality,automatic performance optimization approach,o system,o request,panda parallel,optimization,automatic parallelization,genetic algorithm,computer science,usability goals,resource management,application software,usability,engines,parallel programming,parallel algorithms,resource allocation
Computer science,Parallel algorithm,Usability goals,Resource allocation,Software portability,Application software,Parallel I/O,Genetic algorithm,Distributed computing,Automatic programming
Conference
ISBN
Citations 
PageRank 
0-8186-8579-4
7
0.52
References 
Authors
9
4
Name
Order
Citations
PageRank
Y. Chen18812.81
Marianne Winslett23519744.78
Y. Cho3838.35
S. Kuo4504.66