@Misc{seamons:msio,
  author = {K. E. Seamons and Y. Chen and M. Winslett and Y. Cho and S. Kuo and
  P. Jones and J. Jozwiak and M. Subramanian},
  title = {Fast and Easy {I/O} for Arrays in Large-scale Applications},
  booktitle = {Workshop on Modeling and Specification of I/O},
  year = {1995},
  month = {October},
  note = {At SPDP'95},
  URL = {http://bunny.cs.uiuc.edu/CDR/pubs/msio95.html},
  keywords = {parallel I/O, scientific computing, pario-bib},
  abstract = {This four-page paper, written for an audience from the
  supercomputing/parallel i/o community, is a nice succinct introduction to
  Panda. Abstract and summary: \par Scientists with high-performance computing
  needs are plagued by applications suffering poor i/o performance and are
  burdened with the need to consider low-level physical storage details of
  persistent arrays in order to reach acceptable i/o performance levels,
  especially with existing parallel i/o facilities. The Panda i/o library (URL
  http://bunny.cs.uiuc.edu/CADR/panda.html) serves as a concrete example of a
  methodology for freeing application developers from unnecessary storage
  details through high-level abstract interfaces and providing them with
  increased performance and greater portability. \par Panda addresses these
  problems by introducing high-level application program interfaces for array
  i/o on both parallel and sequential machines, and by developing an efficient
  commodity-parts-based implementation of those interfaces across a variety of
  computer architectures. It is costly to build a file system from scratch and
  we designed Panda to run on top of existing commodity file systems such as
  AIX; excellent performance using this approach implies immediate and broad
  applicability. High-level interfaces provide ease of use, application
  portability, and, most importantly, allow plenty of flexibility for an
  efficient underlying implementation. A high-level view of an entire i/o
  operation, made possible with Panda's high level interfaces, allows Panda to
  optimize reading and writing arrays to the host file system on the i/o nodes
  using Panda's server-directed i/o architecture. \par Panda focuses
  specifically on multidimensional arrays, the data type at the root of i/o
  performance problems in scientific computing. The Panda i/o library exhibits
  excellent performance on the NASA Ames NAS IBM SP2, attaining 83--98\% of
  peak AIX performance on each i/o node in the experiments described in this
  paper. We expect high-level interfaces such as Panda's to become the
  interfaces of choice for scientific applications in the future. As Panda can
  be easily added on top of existing parallel file systems and ordinary file
  systems without changing them, Panda illustrates a way to obtain cheap, fast,
  and easy-to-use i/o for high-performance scientific applications.},
  comment = {Just a short 4-page summary of the Panda I/O library, including
  some brief performance results.}
}