Sparse GPU Kernels for Deep Learning

18 Jun 2020Trevor GaleMatei ZahariaCliff YoungErich Elsen

Scientific workloads have traditionally exploited high levels of sparsity to accelerate computation and reduce memory requirements. While deep neural networks can be made sparse, achieving practical speedups on GPUs is difficult because these applications have relatively moderate levels of sparsity that are not sufficient for existing sparse kernels to outperform their dense counterparts... (read more)

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