Parallelizing and Optimizing Sparse Tensor Computations

Irregular computations over large-scale sparse data are prevalent in critical data applications and they have significant room for improvement on modern computer systems from the aspects of parallelism and data locality. We introduce new techniques to efficiently map large irregular computations with multi-dimensional sparse arrays (or sparse tensors) onto modern multi-core systems with non-uniform memory access (NUMA) behavior. We implement a static-cum-dynamic task scheduling scheme with low overhead for effective parallelization of sparse computations.


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