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Automatic Generation of Efficient Sparse Tensor Format Conversion Routines
[article]
2020
arXiv
pre-print
This paper shows how to generate code that efficiently converts sparse tensors between disparate storage formats (data layouts) like CSR, DIA, ELL, and many others. We decompose sparse tensor conversion into three logical phases: coordinate remapping, analysis, and assembly. We then develop a language that precisely describes how different formats group together and order a tensor's nonzeros in memory. This enables a compiler to emit code that performs complex reorderings (remappings) of
arXiv:2001.02609v1
fatcat:rheu56tyhfcazazxwov5voljc4