New abstractions for data parallel programming

James C. Brodman, Basilio B. Fraguela, María J. Garzarán, David Padua

Research output: Contribution to conferencePaperpeer-review


Developing applications is becoming increasingly difficult due to recent growth in machine complexity along many dimensions, especially that of parallelism. We are studying data types that can be used to represent data parallel operations. Developing parallel programs with these data types have numerous advantages and such a strategy should facilitate parallel programming and enable portability across machine classes and machine generations without significant performance degradation. In this paper, we discuss our vision of data parallel programming with powerful abstractions. We first discuss earlier work on data parallel programming and list some of its limitations. Then, we introduce several dimensions along which is possible to develop more powerful data parallel programming abstractions. Finally, we present two simple examples of data parallel programs that make use of operators developed as part of our studies.

Original languageEnglish (US)
StatePublished - 2009
Event1st USENIX Workshop on Hot Topics in Parallelism, HotPar 2009 - Berkeley, United States
Duration: Mar 30 2009Mar 31 2009


Conference1st USENIX Workshop on Hot Topics in Parallelism, HotPar 2009
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics


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