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A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra
[article]
2019
arXiv
pre-print
We address the problem of optimizing mixed sparse and dense tensor algebra in a compiler. We show that standard loop transformations, such as strip-mining, tiling, collapsing, parallelization and vectorization, can be applied to irregular loops over sparse iteration spaces. We also show how these transformations can be applied to the contiguous value arrays of sparse tensor data structures, which we call their position space, to unlock load-balanced tiling and parallelism. We have prototyped
arXiv:2001.00532v1
fatcat:qdaa7blqmjgctcgyy6ogouod3q