2026-09-19T00:17:31Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/551212022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Saman Amarasinghe. Ansel, Jason (Jason Andrew) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2010-05-25T20:50:25Z 2010-05-25T20:50:25Z 2009 2009 http://hdl.handle.net/1721.1/55121 593573283 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 55-60). It is often impossible to obtain a one-size-fits-all solution for high performance algorithms when considering different choices for data distributions, parallelism, transformations, and blocking. The best solution to these choices is often tightly coupled to different architectures, problem sizes, data, and available system resources. In some cases, completely different algorithms may provide the best performance. Current compiler and programming language techniques are able to change some of these parameters, but today there is no simple way for the programmer to express or the compiler to choose different algorithms to handle different parts of the data. Existing solutions normally can handle only coarse-grained, library level selections or hand coded cutoffs between base cases and recursive cases. We present PetaBricks, a new implicitly parallel language and compiler where having multiple implementations of multiple algorithms to solve a problem is the natural way of programming. We make algorithmic choice a first class construct of the language. Choices are provided in a way that also allows our compiler to tune at a finer granularity. The PetaBricks compiler autotunes programs by making both fine-grained as well as algorithmic choices. Choices also include different automatic parallelization techniques, data distributions, algorithmic parameters, transformations, and blocking. by Jason Ansel. S.M. 60 p. 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. PetaBricks : a language and compiler for algorithmic choice Peta Bricks : a language and compiler for algorithmic choice Language and compiler for algorithmic choice Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e98d571e-8961-47a5-a7ed-1c525351d5a5"> <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>PetaBricks : a language and compiler for algorithmic choice</Title> <Subtitle>Peta Bricks : a language and compiler for algorithmic choice</Subtitle> <Subtitle>Language and compiler for algorithmic choice</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2009</PublicationDate> <Authors> <Author> <DisplayName>Ansel, Jason (Jason Andrew)</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>It is often impossible to obtain a one-size-fits-all solution for high performance algorithms when considering different choices for data distributions, parallelism, transformations, and blocking. The best solution to these choices is often tightly coupled to different architectures, problem sizes, data, and available system resources. In some cases, completely different algorithms may provide the best performance. Current compiler and programming language techniques are able to change some of these parameters, but today there is no simple way for the programmer to express or the compiler to choose different algorithms to handle different parts of the data. Existing solutions normally can handle only coarse-grained, library level selections or hand coded cutoffs between base cases and recursive cases. We present PetaBricks, a new implicitly parallel language and compiler where having multiple implementations of multiple algorithms to solve a problem is the natural way of programming. We make algorithmic choice a first class construct of the language. Choices are provided in a way that also allows our compiler to tune at a finer granularity. The PetaBricks compiler autotunes programs by making both fine-grained as well as algorithmic choices. Choices also include different automatic parallelization techniques, data distributions, algorithmic parameters, transformations, and blocking.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>