SURAA: A Novel Method and Tool for Loadbalanced and Coalesced SpMV Computations on GPUs

Sparse matrix-vector (SpMV) multiplication is a vital building block for numerous scientific and engineering applications. This paper proposes SURAA (translates to speed in arabic), a novel method for SpMV computations on graphics processing units (GPUs). The novelty lies in the way we group matrix...

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Main Authors: Thaha Muhammed, Rashid Mehmood, Aiiad Albeshri, Iyad Katib
Formato: Artigo
Idioma:Inglês
Publicado em: MDPI AG 2019-03-01
Colecção:Applied Sciences
Assuntos:
sparse matrix-vector multiplication (SpMV)
high performance computing (HPC)
graphics processing units
general-purpose computing on graphics processing units (GPGPUs)
iterative methods
data analysis
sparse matrix storage
load balancing
coalesced memory access
thread divergence
Freedman–Diaconis rule
Acesso em linha:http://www.mdpi.com/2076-3417/9/5/947
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