Globally homogeneous, locally adaptive sparse matrix-vector multiplication on the GPU

ICS 2017
Pages: 13
Published: Jun 14, 2017
Abstract
The rising popularity of the graphics processing unit (GPU) across various numerical computing applications triggered a breakneck race to optimize key numerical kernels and in particular, the sparse matrix-vector product (SpMV). Despite great strides, most existing GPU-SpMV approaches trade off one aspect of performance against another. They either require preprocessing, exhibit inconsistent behavior, lead to execution divergence, suffer load...
Paper Details
Title
Globally homogeneous, locally adaptive sparse matrix-vector multiplication on the GPU
Published Date
Jun 14, 2017
Journal
Pages
13
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