@InProceedings{chen:automatic,
  author = {Ying Chen and Marianne Winslett and Y. Cho and S. Kuo},
  title = {Automatic Parallel {I/O} Performance Optimization Using Genetic
  Algorithms},
  booktitle = {Proceedings of the Seventh IEEE International Symposium on High
  Performance Distributed Computing},
  year = {1998},
  month = {July},
  pages = {155--162},
  publisher = {IEEE Computer Society Press},
  URL = {http://www.computer.org/proceedings/hpdc/8579/85790155abs.htm},
  keywords = {parallel I/O, performance optimization, genetic algorithm,
  pario-bib},
  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.}
}