Sparse tensor transpositions in the tensor algebra compiler
Name
1237530529-MIT.pdf
Size
1.19 MB
Format
Adobe PDF
Checksum (MD5)
81ace6ba688310bc48972deaf2ccc09d
Author(s)
Mueller, Suzanne A.
Advisor(s)
Saman Amarasinghe.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
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