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Saman P. Amarasinghe. Wong, Jeremy Ng, 1981- Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2005-06-02T19:35:23Z 2005-06-02T19:35:23Z 2004 2004 http://hdl.handle.net/1721.1/18004 57204290 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. Includes bibliographical references (p. 109-110). Despite the fact that the streaming application domain is becoming increasingly widespread, few studies have focused specifically on the performance characteristics of stream programs. We introduce two models by which the scalability of stream programs can be predicted to some degree of accuracy. This is accomplished by testing a series of stream benchmarks on our numerical representations of the two models. These numbers are then compared to actual speedups obtained by running the benchmarks through the Raw machine and a Magic network. Using the metrics, we show that stateless acyclic stream programs benefit considerably from data, parallelization. In particular, programs with low communication datarates experience up to a tenfold speedup increase when parallelized to a reasonable margin. Those with high communication data rates also experience approximately a twofold speedup. We find that the model that takes synchronization communication overhead into account, in addition to a cost proportional to the communication rate of the stream, provides the highest predictive accuracy. by Jeremy Ng Wong. M.Eng. 110 p. 6072218 bytes 6085525 bytes application/pdf application/pdf eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Modeling the scalability of acrylic stream programs Characterizing the streaming application domain Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e30b8f69-7434-4fe5-95a6-29ed7294ee1e"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Modeling the scalability of acrylic stream programs</Title> <Subtitle>Characterizing the streaming application domain</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2004</PublicationDate> <Authors> <Author> <DisplayName>Wong, Jeremy Ng, 1981-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Despite the fact that the streaming application domain is becoming increasingly widespread, few studies have focused specifically on the performance characteristics of stream programs. We introduce two models by which the scalability of stream programs can be predicted to some degree of accuracy. This is accomplished by testing a series of stream benchmarks on our numerical representations of the two models. These numbers are then compared to actual speedups obtained by running the benchmarks through the Raw machine and a Magic network. Using the metrics, we show that stateless acyclic stream programs benefit considerably from data, parallelization. In particular, programs with low communication datarates experience up to a tenfold speedup increase when parallelized to a reasonable margin. Those with high communication data rates also experience approximately a twofold speedup. We find that the model that takes synchronization communication overhead into account, in addition to a cost proportional to the communication rate of the stream, provides the highest predictive accuracy.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>