@Article{yu:modeling,
  author = {S. Yu and M. Winslett and J. Lee and X. Ma},
  title = {Automatic and Portable Performance Modeling for Parallel {I/O}: A
  Machine-Learning Approach},
  journal = {ACM SIGMETRICS Performance Evaluation Review},
  year = {2002},
  month = {December},
  volume = {30},
  number = {3},
  pages = {3--5},
  publisher = {ACM Press},
  URL = {http://doi.acm.org/10.1145/605521.605524},
  keywords = {parallel I/O, performance model, pario-bib},
  abstract = {A performance model for a parallel I/O system is essential for
  detailed performance analyses, automatic performance optimization of I/O
  request handling, and potential performance bottleneck identification. Yet
  how to build a portable performance model for parallel I/O system is an open
  problem. In this paper, we present a machine-learning approach to automatic
  performance modeling for parallel I/O systems. Our approach is based on the
  use of a platform- independent performance metamodel, which is a radial basis
  function neural network. Given training data, the metamodel generates a
  performance model automatically and efficiently for a parallel I/O system on
  a given platform. Experiments suggest that our goal of having the generated
  model provide accurate performance predictions is attainable, for the
  parallel I/O library that served as our experimental testbed on an IBM SP.
  This suggests that it is possible to model parallel I/O system performance
  automatically and portably, and perhaps to model a broader class of storage
  systems as well.}
}