NDLI: Exploiting coarse-grained task, data, and pipeline parallelism in stream programs (CARN, Vol. 34, Issue. 5)

Content Provider	ACM Digital Library
Author	Gordon, Michael I. Thies, William Amarasinghe, Saman
Abstract	As multicore architectures enter the mainstream, there is a pressing demand for high-level programming models that can effectively map to them. Stream programming offers an attractive way to expose coarse-grained parallelism, as streaming applications (image, video, DSP, etc.) are naturally represented by independent filters that communicate over explicit data channels.In this paper, we demonstrate an end-to-end stream compiler that attains robust multicore performance in the face of varying application characteristics. As benchmarks exhibit different amounts of task, data, and pipeline parallelism, we exploit all types of parallelism in a unified manner in order to achieve this generality. Our compiler, which maps from the StreamIt language to the 16-core Raw architecture, attains a 11.2x mean speedup over a single-core baseline, and a 1.84x speedup over our previous work.
Starting Page	151
Ending Page	162
Page Count	12
File Format	PDF
ISSN	01635964
DOI	10.1145/1168919.1168877
Journal	ACM SIGARCH Computer Architecture News (CARN)
Volume Number	34
Issue Number	5
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1981-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multicore Streamit Raw Coarse-grained dataflow Software pipelining Streams
Content Type	Text
Resource Type	Article

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Exploiting coarse-grained task, data, and pipeline parallelism in stream programs (CARN, Vol. 34, Issue. 5)