Generating optimal CUDA sparse matrix-vector product implementations for evolving GPU hardware
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]
|Collections||ANU Research Publications|
|Source:||Concurrency and Computation: Practice and Experience|
|01_El Zein_Generating_optimal_CUDA_sparse_2012.pdf||2.38 MB||Adobe PDF||Request a copy|
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