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Title: Design Principles for Sparse Matrix Multiplication on the GPU
We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row (CSR) format and thus do not require expensive format conversion. While previous SpMM work concentrates on thread-level parallelism, we additionally focus on latency hiding with instruction-level parallelism and load-balancing. We show, both theoretically and experimentally, that the proposed SpMM is a better fit for the GPU than previous approaches. We identify a key memory access pattern that allows efficient access into both input and output matrices that is crucial to getting excellent performance on SpMM. By combining these two ingredients---(i) merge-based load-balancing and (ii) row-major coalesced memory access---we demonstrate a 4.1x peak speedup and a 31.7% geomean speedup over state-of-the-art SpMM implementations on real-world datasets.  more » « less
Award ID(s):
1629657
NSF-PAR ID:
10066978
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Euro-Par 2018: Proceedings of the 24th International European Conference on Parallel and Distributed Computing
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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