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  4. Finch: Sparse and Structured Tensor Programming with Control Flow

Finch: Sparse and Structured Tensor Programming with Control Flow

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Author(s)
Ahrens, Willow
•
Collin, Teodoro
•
Patel, Radha
•
Deeds, Kyle
•
Hong, Changwan
•
Amarasinghe, Saman
Date Issued
April 9, 2025
Journal
Proceedings of the ACM on Programming Languages
Publisher
ACM
Citation
Willow Ahrens, Teodoro Fields Collin, Radha Patel, Kyle Deeds, Changwan Hong, and Saman Amarasinghe. 2025. Finch: Sparse and Structured Tensor Programming with Control Flow. Proc. ACM Program. Lang. 9, OOPSLA1, Article 117 (April 2025), 31 pages.
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Final published version
Abstract
From FORTRAN to NumPy, tensors have revolutionized how we express computation. However, tensors in these, and almost all prominent systems, can only handle dense rectilinear integer grids. Real world tensors often contain underlying structure, such as sparsity, runs of repeated values, or symmetry. Support for structured data is fragmented and incomplete. Existing frameworks limit the tensor structures and program control flow they support to better simplify the problem. In this work, we propose a new programming language, Finch, which supports both flexible control flow and diverse data structures. Finch facilitates a programming model which resolves the challenges of computing over structured tensors by combining control flow and data structures into a common representation where they can be co-optimized. Finch automatically specializes control flow to data so that performance engineers can focus on experimenting with many algorithms. Finch supports a familiar programming language of loops, statements, ifs, breaks, etc., over a wide variety of tensor structures, such as sparsity, run-length-encoding, symmetry, triangles, padding, or blocks. Finch reliably utilizes the key properties of structure, such as structural zeros, repeated values, or clustered non-zeros. We show that this leads to dramatic speedups in operations such as SpMV and SpGEMM, image processing, and graph analytics.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
https://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/164689
DOI of Published Version
https://doi.org/10.1145/3720473
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