TY - CONF
T1 - New abstractions for data parallel programming
AU - Brodman, James C.
AU - Fraguela, Basilio B.
AU - Garzarán, María J.
AU - Padua, David
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Awards CCF 0702260, CNS 0509432, and by the Universal Parallel Computing Research Center at the University of Illinois at Urbana-Champaign, sponsored by INTEL Corporation and Microsoft Corporation. Basilio B. Fraguela was partially supported by the Xunta de Galicia under project INCITE08PXIB105161PR and the Ministry of Education and Science of Spain, FEDER funds of the European Union (Project TIN2007-67537-C03-02).
Publisher Copyright:
© HotPar 2009.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
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M3 - Paper
AN - SCOPUS:84962873219
T2 - 1st USENIX Workshop on Hot Topics in Parallelism, HotPar 2009
Y2 - 30 March 2009 through 31 March 2009
ER -