@Article{davis:rle,
  author = {G. Davis and L. Lau and R. Young and F. Duncalfe and L. Brebber},
  title = {Parallel Run-Length Encoding {(RLE)} Compression---Reducing {I/O} in
  Dynamic Environmental Simulations},
  journal = {The International Journal of High Performance Computing
  Applications},
  year = {1998},
  month = {Winter},
  volume = {12},
  number = {4},
  pages = {396--410},
  note = {In a Special Issue on I/O in Parallel Applications},
  keywords = {parallel I/O application, compression, pario-bib},
  abstract = {Dynamic simulations based on time-varying inputs are extremely
  I/O intensive. This is shown by industrial applications generating
  environmental projections based on seasonal-to-interannual climate forecasts
  which have a compute to data-access ratio of O(n) leading to significant
  performance degradation. Exploitation of compression techniques such as
  Run-Length-Encoding (RLE) significantly reduces the I/O bottleneck and
  storage requirements. Unfortunately, traditional RLE algorithms do not
  perform well in a parallel-vector platform such as the Cray architecture.
  This paper describes the design and implementation of a new RLE algorithm
  based on data chunking and packing that exploits the Cray gather-scatter
  vector hardware and multiple processors. This innovative approach reduces I/O
  and file storage requirements on average by an order of magnitude. Data
  intensive applications such as the integration of environmental and global
  climate models now become practical in a realistic time-frame.},
  comment = {In a Special Issue on I/O in Parallel Applications, volume 12,
  numbers 3 and 4.}
}