2026-09-19T21:15:29Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1230412026-06-06T00:49:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Saman Amarasinghe.
Noyola, Patricio.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-22T00:03:54Z
2019-11-22T00:03:54Z
2019
2019
https://hdl.handle.net/1721.1/123041
1127908279
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 65-66 ).
Tensor algebra is a powerful tool for computing on multidimensional data and has applications in many fields. The number of possible tensor operations is infinite, so it is impossible to manually implement all of them to operate on different tensor dimensions. The Tensor Algebra Compiler (taco) introduced a compiler approach to automatically generate kernels for any compound tensor algebra operation on any input tensor formats. In this thesis, we present a new API for the taco library. The API removes the need to call compiler methods with the introduction of a delayed execution framework. Additionally, the API introduces multiple important tensor algebra features previously unavailable in taco. Finally, we propose extensions to taco's code generation algorithm to automatically generate tensor API methods for any tensor format. The proposed API makes taco code cleaner, shorter and more elegant. Furthermore, the extensions to its code generation algorithm make the API scalable to new formats and operations.
by Patricio Noyola.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
90 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.
A Tensor Algebra Compiler library interface and runtime
TACO library interface and runtime
Thesis
application/pdf
2019-11-22T00:03:53Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Tensor algebra is a powerful tool for computing on multidimensional data and has applications in many fields. The number of possible tensor operations is infinite, so it is impossible to manually implement all of them to operate on different tensor dimensions. The Tensor Algebra Compiler (taco) introduced a compiler approach to automatically generate kernels for any compound tensor algebra operation on any input tensor formats. In this thesis, we present a new API for the taco library. The API removes the need to call compiler methods with the introduction of a delayed execution framework. Additionally, the API introduces multiple important tensor algebra features previously unavailable in taco. Finally, we propose extensions to taco's code generation algorithm to automatically generate tensor API methods for any tensor format. The proposed API makes taco code cleaner, shorter and more elegant. Furthermore, the extensions to its code generation algorithm make the API scalable to new formats and operations.</Abstract>
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