SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning. (arXiv:2207.04606v2 [cs.LG] UPDATED)

Sparse tensors are rapidly becoming critical components of modern deep
learning workloads. However, developing high-performance sparse operators can
be difficult and tedious, and existing vendor libraries cannot satisfy the
escalating demands from new operators. Sparse tensor compilers simplify the
development of operators, but efficient sparse compilation for deep learning
remains challenging because a single sparse format cannot maximize hardware
efficiency, and single-shot compilers cannot keep up with latest hardware and
system advances. We show that the key to addressing both …

arxiv deep learning learning lg