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Optimizing the SpMV kernel on long-vector accelerators

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hdl:2117/346295

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Gómez Crespo, Constantino
Mantovani, FilippoMés informació
Focht, Erich
Casas, MarcMés informació
Document typeConference report
Defense date2021-05
PublisherBarcelona Supercomputing Center
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
Sparse Matrix-Vector multiplication (SpMV) is an essential kernel for parallel numerical applications. SpMV displays sparse and irregular data accesses, which complicate its vectorization. Such difficulties make SpMV to frequently experiment non-optimal results when run on long vector ISAs exploiting SIMD parallelism. In this context, the development of new optimizations becomes fundamental to enable high performance SpMV executions on emerging long vector architectures. In our work, we improve the state-of-the-art SELL-C- sparse matrix format by proposing several new optimizations for SpMV. We target aggressive long vector architectures like the NEC Vector Engine. By combining several optimizations, we obtain an average 12% improvement over SELL-C- considering a heterogeneous set of 24 matrices. Our optimizations boost performance in long vector architectures since they expose a high degree of SIMD parallelism.
CitationGómez Crespo, C. [et al.]. Optimizing the SpMV kernel on long-vector accelerators. A: . Barcelona Supercomputing Center, 2021, p. 30-31. 
URIhttp://hdl.handle.net/2117/346295
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