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Fredrik Kjolstad,
Shoaib Kamil,
Stephen Chou,
David Lugato
and
Saman Amarasinghe


The Tensor Algebra Compiler
OOPSLA, October 2017.
Abstract
Tensor algebra is a powerful tool with applications in machine
learning, data analytics, engineering and the physical sciences.
Tensors are often sparse and compound operations must frequently be
computed in a single kernel for performance and to save memory.
Programmers are left to write kernels for every operation of
interest, with different mixes of dense and sparse tensors in
different formats. The combinations are infinite, which makes it
impossible to manually implement and optimize them all. This paper
introduces the first compiler technique to automatically generate
kernels for any compound tensor algebra operation on dense and sparse
tensors. The technique is implemented in a C++ library called
taco. Its performance is competitive with bestinclass
handoptimized kernels in popular libraries, while supporting far
more tensor operations.
Documents
download article:
BibTeX 
@article{kjolstad2017taco,
author={Kjolstad, Fredrik and Kamil, Shoaib and Chou, Stephen and Lugato, David and Amarasinghe, Saman},
title={{The Tensor Algebra Compiler}},
journal={Proceedings of the ACM on Programming Languages},
volume={1},
number={OOPSLA},
year={2017},
month={October}
}


