@InProceedings{li:models,
  author = {Zhiyong Li and Peter H. Mills and John H. Reif},
  title = {Models and Resource Metrics for Parallel and Distributed
  Computation},
  booktitle = {Proceedings of the Twenty-Eighth Annual Hawaii International
  Conference on System Sciences},
  year = {1995},
  month = {January},
  pages = {51--60},
  address = {Hawaii},
  later = {li:jmodels},
  URL =
  {file://ftp.cs.unc.edu/pub/projects/proteus/reports/models_hicss95.ps.gz"},
  keywords = {parallel I/O algorithm, pario-bib},
  abstract = {This paper presents a framework of using {\em resource metrics}
  to characterize the various models of parallel computation. Our framework
  reflects the approach of recent models to abstract architectural details into
  several generic parameters, which we call resource metrics. We examine the
  different resource metrics chosen by different parallel models, categorizing
  the models into four classes: the basic synchronous models, and extensions of
  the basic models which more accurately reflect practical machines by
  incorporating notions of asynchrony, communication cost and memory hierarchy.
  We then present a new parallel computation model, the LogP-HMM model, as an
  illustration of design principles based on the framework of resource metrics.
  The LogP-HMM model extends an existing parameterized network model (LogP)
  with a sequential hierarchical memory model (HMM) characterizing each
  processor. The result accurately captures both network communication costs
  and the effects of multileveled memory such as local cache and I/O. We
  examine the potential utility of our model in the design of near optimal
  sorting and FFT algorithms.}
}