2026-09-19T04:58:56Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1501852023-04-01T03:03:19Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Amarasinghe, Saman
Dima, Alexandra
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-03-31T14:38:17Z
2023-03-31T14:38:17Z
2023-02
2023-02-27T18:43:28.479Z
https://hdl.handle.net/1721.1/150185
Many applications in engineering and computer science are characterized by sparse multi-dimensional data. Therefore, optimizations for sparse tensor algebra have received a lot of attention lately. Several hardware and software solutions have emerged in order to speed up the computation of certain tensor expressions, but none of them provides an interface that is general and comprehensive enough to meet the requirements of complex applications like graph analysis. In this work we attempt to identify where previous solutions fell short and build the Generalized Sparse Tensor Algebra Compiler (GSTACO), a new compiler aiming to fill in the engineering gaps of efficient sparse computation.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
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http://rightsstatements.org/page/InC-EDU/1.0/
GSTACO: A Generalized Sparse Tensor Algebra Compiler
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Many applications in engineering and computer science are characterized by sparse multi-dimensional data. Therefore, optimizations for sparse tensor algebra have received a lot of attention lately. Several hardware and software solutions have emerged in order to speed up the computation of certain tensor expressions, but none of them provides an interface that is general and comprehensive enough to meet the requirements of complex applications like graph analysis. In this work we attempt to identify where previous solutions fell short and build the Generalized Sparse Tensor Algebra Compiler (GSTACO), a new compiler aiming to fill in the engineering gaps of efficient sparse computation.</Abstract>
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