Affiliation:
1. University of Illinois at Urbana--Champaign, Urbana, IL
2. Google, Mountain View, CA
Abstract
Sparse matrix--matrix multiplication (SpGEMM) is a key operation in numerous areas from information to the physical sciences. Implementing SpGEMM efficiently on throughput-oriented processors, such as the graphics processing unit (GPU), requires the programmer to expose substantial fine-grained parallelism while conserving the limited off-chip memory bandwidth. Balancing these concerns, we decompose the SpGEMM operation into three highly parallel phases: expansion, sorting, and contraction, and introduce a set of complementary bandwidth-saving performance optimizations. Our implementation is fully general and our optimization strategy adaptively processes the SpGEMM workload row-wise to substantially improve performance by decreasing the work complexity and utilizing the memory hierarchy more effectively.
Publisher
Association for Computing Machinery (ACM)
Subject
Applied Mathematics,Software
Cited by
68 articles.
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1. A Survey of Accelerating Parallel Sparse Linear Algebra;ACM Computing Surveys;2023-08-28
2. Efficient Execution of SpGEMM on Long Vector Architectures;Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing;2023-08-07
3. HASpGEMM: Heterogeneity-Aware Sparse General Matrix-Matrix Multiplication on Modern Asymmetric Multicore Processors;Proceedings of the 52nd International Conference on Parallel Processing;2023-08-07
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5. A Machine Learning Approach Towards Runtime Optimisation of Matrix Multiplication;2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2023-05