@InProceedings{chen:panda-automatic,
  author = {Y. Chen and M. Winslett and Y. Cho and S. Kuo},
  title = {Automatic parallel {I/O} performance optimization in {Panda}},
  booktitle = {Proceedings of the Eleventh Symposium on Parallel Algorithms and
  Architectures},
  year = {1998},
  pages = {108--118},
  URL = {http://doi.acm.org/10.1145/277651.277677},
  keywords = {parallel I/O, Panda, portability, pario-bib},
  abstract = {Parallel I/O systems typically consist of individual processors,
  communication networks, and a large number of disks. Managing and utilizing
  these resources to meet performance, portability and usability goals of
  applications has become a significant challenge. 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. Under our
  approach, as optimization engine in a parallel I/O system selects optimal I/O
  plans automatically without human intervention based on a description of the
  application I/O requests and the system configuration. To validate our
  hypothesis, we have built an optimizer that uses a rule-based and randomized
  search-based algorithms to select optimal parameter settings in Panda, a
  parallel I/O library for multidimensional arrays. Our performance results
  obtained from two IBM SPs with significantly different configurations show
  that the Panda optimizer is able to select high-quality I/O plans and deliver
  high performance under a variety of system configurations}
}