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Saman Amarasinghe. Chou, Stephen (Computer scientist) Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-05-23T15:05:16Z 2018-05-23T15:05:16Z 2018 2018 http://hdl.handle.net/1721.1/115625 1036987039 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 99-105). Tensor algebra is a powerful tool for computing on multidimensional data and has applications in many fields. Practical applications often deal with tensors that are sparse, and there exists a wide variety of formats for storing such tensors, each suited to specific types of applications and data. Examples of sparse tensor storage formats include COO, CSR, CSC, DCSR, BCSR, CSF, CSB, ELL, DIA, and hash maps. In this thesis, we propose a levelized hierarchical abstraction that represents these seemingly disparate formats and countless others, and that hides the details of each format behind a common interface. We show that this tensor representation facilitates automatic generation of efficient compute kernels for tensor algebra expressions with any combination of formats. This is accomplished with a code generation algorithm that generates code level by level, guided by the capabilities and properties of the levels. The performance of tensor algebra kernels generated using our technique is competitive with that of equivalent hand-implemented kernels in existing sparse linear and tensor algebra libraries. Furthermore, our technique can generate many more kernels for many more formats than exist in libraries or are supported by existing compiler techniques. by Stephen Chou. S.M. 105 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Unfield sparse formats for tensor algebra compilers Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f3ecfb1b-6f56-4fd9-9a1d-11bbe1615348"> <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>Unfield sparse formats for tensor algebra compilers</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Chou, Stephen (Computer scientist) Massachusetts Institute of Technology</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>Tensor algebra is a powerful tool for computing on multidimensional data and has applications in many fields. Practical applications often deal with tensors that are sparse, and there exists a wide variety of formats for storing such tensors, each suited to specific types of applications and data. Examples of sparse tensor storage formats include COO, CSR, CSC, DCSR, BCSR, CSF, CSB, ELL, DIA, and hash maps. In this thesis, we propose a levelized hierarchical abstraction that represents these seemingly disparate formats and countless others, and that hides the details of each format behind a common interface. We show that this tensor representation facilitates automatic generation of efficient compute kernels for tensor algebra expressions with any combination of formats. This is accomplished with a code generation algorithm that generates code level by level, guided by the capabilities and properties of the levels. The performance of tensor algebra kernels generated using our technique is competitive with that of equivalent hand-implemented kernels in existing sparse linear and tensor algebra libraries. Furthermore, our technique can generate many more kernels for many more formats than exist in libraries or are supported by existing compiler techniques.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>