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dc.contributor.authorGómez Crespo, Constantino
dc.contributor.authorMantovani, Filippo
dc.contributor.authorFocht, Erich
dc.contributor.authorCasas, Marc
dc.identifier.citationGómez Crespo, C. [et al.]. Optimizing the SpMV kernel on long-vector accelerators. A: . Barcelona Supercomputing Center, 2021, p. 30-31.
dc.description.abstractSparse 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.
dc.format.extent2 p.
dc.publisherBarcelona Supercomputing Center
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subject.lcshHigh performance computing
dc.subject.lcshKernel functions
dc.subject.otherLong-Vector Architectures
dc.subject.otherPerformance Optimization
dc.subject.otherNEC Vector Engine
dc.titleOptimizing the SpMV kernel on long-vector accelerators
dc.typeConference report
dc.subject.lemacCàlcul intensiu (Informàtica)
dc.subject.lemacKernel, Funcions de
dc.rights.accessOpen Access

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