A unified iteration space transformation framework for sparse and dense tensor algebra
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2021
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| Online Access: | https://hdl.handle.net/1721.1/129852 |
| _version_ | 1826217563656290304 |
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| author | Senanayake, Ryan Michael. |
| author2 | Saman Amarasinghe. |
| author_facet | Saman Amarasinghe. Senanayake, Ryan Michael. |
| author_sort | Senanayake, Ryan Michael. |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020 |
| first_indexed | 2024-09-23T17:05:40Z |
| format | Thesis |
| id | mit-1721.1/129852 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:05:40Z |
| publishDate | 2021 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1298522021-02-20T03:00:40Z A unified iteration space transformation framework for sparse and dense tensor algebra Senanayake, Ryan Michael. Saman Amarasinghe. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. 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 95-99). This work addresses the problem of optimizing mixed sparse and dense tensor algebra in a compiler. I show that standard loop transformations, such as strip-mining, tiling, collapsing, parallelization and vectorization, can be applied to irregular loops over sparse iteration spaces. I also show how these transformations can be applied to the contiguous value arrays of sparse tensor data structures, which I call their position spaces, to unlock load-balanced tiling and parallelism. These concepts have been prototyped in the open-source TACO system, where they are exposed as a scheduling API similar to the Halide domain-specific language for dense computations. Using this scheduling API, I show how to optimize mixed sparse/dense tensor algebra expressions, how to generate load-balanced code by scheduling sparse tensor algebra in position space, and how to generate sparse tensor algebra GPU code. As shown in the evaluation, these transformations allow us to generate code that is competitive with many hand-optimized implementations from the literature. by Ryan Michael Senanayake. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2021-02-19T20:20:36Z 2021-02-19T20:20:36Z 2020 2020 Thesis https://hdl.handle.net/1721.1/129852 1237564498 eng 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 99 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Senanayake, Ryan Michael. A unified iteration space transformation framework for sparse and dense tensor algebra |
| title | A unified iteration space transformation framework for sparse and dense tensor algebra |
| title_full | A unified iteration space transformation framework for sparse and dense tensor algebra |
| title_fullStr | A unified iteration space transformation framework for sparse and dense tensor algebra |
| title_full_unstemmed | A unified iteration space transformation framework for sparse and dense tensor algebra |
| title_short | A unified iteration space transformation framework for sparse and dense tensor algebra |
| title_sort | unified iteration space transformation framework for sparse and dense tensor algebra |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/129852 |
| work_keys_str_mv | AT senanayakeryanmichael aunifiediterationspacetransformationframeworkforsparseanddensetensoralgebra AT senanayakeryanmichael unifiediterationspacetransformationframeworkforsparseanddensetensoralgebra |