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Generating optimal CUDA sparse matrix-vector product implementations for evolving GPU hardware

El Zein, Ahmed; Rendell, Alistair


The CUDA model for graphics processing units (GPUs) presents the programmer with a plethora of different programming options. These includes different memory types, different memory access methods and different data types. Identifying which options to use and when is a non-trivial exercise. This paper explores the effect of these different options on the performance of a routine that evaluates sparse matrix-vector products (SpMV) across three different generations of NVIDIA GPU hardware. A...[Show more]

CollectionsANU Research Publications
Date published: 2012
Type: Journal article
Source: Concurrency and Computation: Practice and Experience
DOI: 10.1002/cpe.1732


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