Runtime vs. Manual Data Distribution for Architecture-Agnostic Shared-Memory Programming Models

  • Authors:

  • Dimitrios S. Nikolopoulos;Eduard Ayguadé;Constantine D. Polychronopoulos

  • Affiliations:

  • Coordinated Science Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 1308 W. Main St., Urbana, Illinois 61801. dsn@csrd.uiuc.edueduard@ac.upc.es;Coordinated Science Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 1308 W. Main St., Urbana, Illinois 61801. cdp@csrd.uiuc.edu

  • Venue:
  • International Journal of Parallel Programming
  • Year:
  • 2002

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Abstract

This paper compares data distribution methodologies for scaling the performance of OpenMP on NUMA architectures. We investigate the performance of automatic page placement algorithms implemented in the operating system, runtime algorithms based on dynamic page migration, runtime algorithms based on loop scheduling transformations and manual data distribution. These techniques present the programmer with trade-offs between performance and programming effort. Automatic page placement algorithms are transparent to the programmer, but may compromise memory access locality. Dynamic page migration algorithms are also transparent, but require careful engineering and tuned implementations to be effective. Manual data distribution requires substantial programming effort and architecture-specific extensions to the API, but may localize memory accesses in a nearly optimal manner. Loop scheduling transformations may or may not require intervention from the programmer, but conform better to an architecture-agnostic programming paradigm like OpenMP. We identify the conditions under which runtime data distribution algorithms can optimize memory access locality in OpenMP. We also present two novel runtime data distribution techniques, one based on memory access traces and another based on affinity scheduling of parallel loops. These techniques can be used to effectively replace manual data distribution in regular applications. The results provide a proof of concept that it is possible to scale a portable shared-memory programming model up to more than 100 processors, without modifying the API and without exposing architectural details to the programmer.