2026-09-19T14:47:46Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1299192026-06-06T00:56:18Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Saman Amarasinghe.
Mueller, Suzanne A.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-02-19T20:56:56Z
2021-02-19T20:56:56Z
2020
2020
https://hdl.handle.net/1721.1/129919
1237530529
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 89-90).
The current state of the art for transposing sparse tensors involves converting the sparse tensor into a list of coordinates, sorting the list of coordinates and finally packing the list of coordinates into the desired sparse tensor format. This thesis explores the potential for faster methodologies. Its main contributions are an algorithm that exploits partial sortedness to minimize sorting passes and an implementation that demonstrates that this transposition algorithm is competitive with state of the art. In particular the algorithm takes advantage of the ordering that already exists to apply selective sorting passes and thereby reduce the amount of work that needs to be done to reorder the tensor.
by Suzanne A. Mueller.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
90 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Sparse tensor transpositions in the tensor algebra compiler
Thesis
application/pdf
2021-02-19T20:56:26Z
Publication
Master
EECS
unknown
unknown
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