@PhdThesis{seamons:thesis,
  author = {Kent E. Seamons},
  title = {Panda: Fast Access to Persistent Arrays Using High Level Interfaces
  and Server Directed Input/Output},
  year = {1996},
  month = {May},
  school = {University of Illinois at Urbana-Champaign},
  URL = {http://bunny.cs.uiuc.edu/CADR/pubs/seamons-thesis.html},
  keywords = {parallel I/O, persistent data, parallel computing, pario-bib},
  abstract = {Multidimensional arrays are a fundamental data type in scientific
  computing and are used extensively across a broad range of applications.
  Often these arrays are persistent, i.e., they outlive the invocation of the
  program that created them. Portability and performance with respect to input
  and output (i/o) pose significant challenges to applications accessing large
  persistent arrays, especially in distributed-memory environments. A
  significant number of scientific applications perform conceptually simple
  array i/o operations, such as reading or writing a subarray, an entire array,
  or a list of arrays. However, the algorithms to perform these operations
  efficiently on a given platform may be complex and non-portable, and may
  require costly customizations to operating system software. \par This thesis
  presents a high-level interface for array i/o and three implementation
  architectures, embodied in the Panda (Persistence AND Arrays) array i/o
  library. The high-level interface contributes to application portability, by
  encapsulating unnecessary details and being easy to use. Performance results
  using Panda demonstrate that an i/o system can provide application programs
  with a high-level, portable, easy-to-use interface for array i/o without
  sacrificing performance or requiring custom system software; in fact,
  combining all these benefits may only be possible through a high-level
  interface due to the great freedom and flexibility a high-level interface
  provides for the underlying implementation. \par The Panda server-directed
  i/o architecture is a prime example of an efficient implementation of
  collective array i/o for closely synchronized applications in
  distributed-memory single-program multiple-data (SPMD) environments. A
  high-level interface is instrumental to the good performance of
  server-directed i/o, since it provides a global view of an upcoming
  collective i/o operation that Panda uses to plan sequential reads and writes.
  Performance results show that with server-directed i/o, Panda achieves
  throughputs close to the maximum AIX file system throughput on the i/o nodes
  of the IBM SP2 when reading and writing large multidimensional arrays.},
  comment = {see also chen:panda, seamons:panda, seamons:compressed,
  seamons:interface, seamons:schemas, seamons:msio, seamons:jpanda}
}