Implementation of a Portable Nested Data-Parallel Language

Implementation of a Portable Nested Data-Parallel Language (1994)

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by Guy Blelloch , Siddhartha Chatterjee , Jonathan C. Hardwick , Jay Sipelstein , Marco Zagha

Venue:	Journal of Parallel and Distributed Computing
Citations:	155 - 26 self

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Datum	Value	Source
TITLE	Implementation of a Portable Nested Data-Parallel Language	user correction - Legacy Corrections
AUTHOR NAME	Guy Blelloch	user correction - Legacy Corrections
AUTHOR AFFIL	1	user correction - Legacy Corrections
AUTHOR NAME	Siddhartha Chatterjee	user correction - Legacy Corrections
AUTHOR AFFIL	2	user correction - Legacy Corrections
AUTHOR NAME	Jonathan C. Hardwick	user correction - Legacy Corrections
AUTHOR AFFIL	RIACS, Mail Stop T045-1, NASA Ames Research Center, Moffett	user correction - Legacy Corrections
AUTHOR ADDR	Field, CA 94035	user correction - Legacy Corrections
AUTHOR NAME	Jay Sipelstein	user correction - Legacy Corrections
AUTHOR AFFIL	RIACS, Mail Stop T045-1, NASA Ames Research Center, Moffett	user correction - Legacy Corrections
AUTHOR ADDR	Field, CA 94035	user correction - Legacy Corrections
AUTHOR NAME	Marco Zagha	user correction - Legacy Corrections
AUTHOR AFFIL	RIACS, Mail Stop T045-1, NASA Ames Research Center, Moffett	user correction - Legacy Corrections
AUTHOR ADDR	Field, CA 94035	user correction - Legacy Corrections
ABSTRACT	This paper gives an overview of the implementation of Nesl, a portable nested data-parallel language. This language and its implementation are the first to fully support nested data structures as well as nested dataparallel function calls. These features allow the concise description of parallel algorithms on irregular data, such as sparse matrices and graphs. In addition, they maintain the advantages of data-parallel languages: a simple programming model and portability. The current Nesl implementation is based on an intermediate language called Vcode and a library of vector routines called Cvl. It runs on the Connection Machine CM-2, the Cray Y-MP C90, and serial machines. We compare initial benchmark results of Nesl with those of machine-specific code on these machines for three algorithms: least-squares line-fitting, median finding, and a sparse-matrix vector product. These results show that Nesl's performance is competitive with that of machine-specific codes for regular dense da...	user correction - Legacy Corrections
YEAR	1994	INFERENCE
VENUE	Journal of Parallel and Distributed Computing	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	102--111	INFERENCE
VOLUME	21	INFERENCE
CITATIONS	14 found	ParsCit 1.0