Unified Compilation for Lossless Compression and Sparse Computing

This paper shows how to extend sparse tensor algebra compilers to support lossless compression techniques, including variants of run-length encoding and Lempel-Ziv compression. We develop new abstractions to represent losslessly compressed data as a generalized form of sparse tensors, with repetitio...

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Vydáno v:2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) s. 205 - 216
Hlavní autoři: Donenfeld, Daniel, Chou, Stephen, Amarasinghe, Saman
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 02.04.2022
Témata:
Algebra
Codes
compressed domain processing
Computational efficiency
Image coding
Kernel
Libraries
lossless compression
sparse tensor algebra
Tensors
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Shrnutí:This paper shows how to extend sparse tensor algebra compilers to support lossless compression techniques, including variants of run-length encoding and Lempel-Ziv compression. We develop new abstractions to represent losslessly compressed data as a generalized form of sparse tensors, with repetitions of values (which are compressed out in storage) represented by non-scalar, dynamic fill values. We then show how a compiler can use these abstractions to emit efficient code that computes on losslessly compressed data. By unifying lossless compression with sparse tensor algebra, our technique is able to generate code that computes with both losslessly compressed data and sparse data, as well as generate code that computes directly on compressed data without needing to first decompress it.Our evaluation shows our technique generates efficient image and video processing kernels that compute on losslessly compressed data. We find that the generated kernels are up to 16. 3× faster than equivalent dense kernels generated by TACO, a tensor algebra compiler, and up to 16. 1× faster than OpenCV, a widely used image processing library.
DOI:10.1109/CGO53902.2022.9741282