Supporting Irregular Distributions Using Data-Parallel Languages

  • Authors:
  • Ravi Ponnusamy;Yuan-Shin Hwang;Raja Das;Joel H. Saltz;Alok Choudhary;Geoffrey Fox

  • Affiliations:
  • -;-;-;-;-;-

  • Venue:
  • IEEE Parallel & Distributed Technology: Systems & Technology
  • Year:
  • 1995

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Abstract

This article presents methods that make it possible to efficiently support irregular problems using data-parallel languages. The approach involves the use of a portable, compiler-independent, runtime support library called CHAOS. The CHAOS runtime support library contains procedures that support static and dynamic distributed array partitioning, partition loop iterations and indirection arrays, remap arrays from one distribution to another, and carry out index translation, buffer allocation and communication schedule generation. The CHAOS runtime procedures are used by a prototype Fortran 90D compiler as runtime support for irregular problems. This article also presents performance results of compiler-generated and hand-parallelized versions of two stripped-down applications codes. The first code is derived from an unstructured mesh computational fluid dynamics flow solver and the second is derived from the molecular dynamics code CHARMM. A method is described that makes it possible to emulate irregular distributions in HPF by reordering elements of data arrays and renumbering indirection arrays. The results suggest that an HPF compiler could use reordering and renumbering extrinsic functions to obtain performance comparable to that achieved by a compiler for a language (such as Fortran 90D) that directly supports irregular distributions.