The performances of R GPU implementations of the GMRES method
Although the performance of commodity computers has improved drastically with the introduction of multicore processors and GPU computing, the standard R distribution is still based on single-threaded model of computation, using only a small fraction of the computational power available now for most...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Romanian National Institute of Statistics
2018-03-01
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| Series: | Revista Română de Statistică |
| Subjects: | |
| Online Access: | http://www.revistadestatistica.ro/wp-content/uploads/2018/03/RRS_1_2018_A09.pdf |
| Summary: | Although the performance of commodity computers has improved drastically with the introduction of multicore processors and GPU computing, the standard R distribution is still based on single-threaded model of computation, using only a small fraction of the computational power available now for most desktops and laptops. Modern statistical software packages rely on high performance implementations of the linear algebra routines there are at the core of several important leading edge statistical methods. In this paper we present a GPU implementation of the GMRES iterative method for solving linear systems. We compare the performance of this implementation with a pure single threaded version of the CPU. We also investigate the performance of our implementation using different GPU packages available now for R such as gmatrix, gputools or gpuR which are based on CUDA or OpenCL frameworks. |
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| ISSN: | 1018-046X 1844-7694 |