@Article{madhyastha:learning,
  author = {Tara M. Madhyastha and Daniel A. Reed},
  title = {Learning to Classify Parallel Input/Output Access Patterns},
  journal = {IEEE Transactions on Parallel and Distributed Systems},
  year = {2002},
  month = {August},
  volume = {13},
  number = {8},
  pages = {802--813},
  URL = {http://computer.org/tpds/td2002/l0802abs.htm},
  keywords = {parallel I/O, file access pattern, pario-bib},
  abstract = {Input/output performance on current parallel file systems is
  sensitive to a good match of application access patterns to file system
  capabilities. Automatic input/output access pattern classification can
  determine application access patterns at execution time, guiding adaptive
  file system policies. In this paper, we examine and compare two novel
  input/output access pattern classification methods based on learning
  algorithms. The first approach uses a feedforward neural network previously
  trained on access pattern benchmarks to generate qualitative classifications.
  The second approach uses hidden Markov models trained on access patterns from
  previous executions to create a probabilistic model of input/output accesses.
  In a parallel application, access patterns can be recognized at the level of
  each local thread or as the global interleaving of all application threads.
  Classification of patterns at both levels is important for parallel file
  system performance; we propose a method for forming global classifications
  from local classifications. We present results from parallel and sequential
  benchmarks and applications that demonstrate the viability of this approach.}
}