The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.05 - September/October (2009 vol.11)
pp: 58-63
ABSTRACT
<p>Modern GPUs are massively parallel microprocessors that can deliver very high performance for the parallel computations common in science and engineering.</p>
CITATION
Jonathan Cohen, Michael Garland, "Solving Computational Problems with GPU Computing", Computing in Science & Engineering, vol.11, no. 5, pp. 58-63, September/October 2009, doi:10.1109/MCSE.2009.144
REFERENCES
1. J.D. Owens et al., "A Survey of General-Purpose Computation on Graphics Hardware," Computer Graphics Forum, vol. 26, no. 1, 2007, pp. 80–113.
2. E. Lindholm et al., "Nvidia Tesla: A Unified Graphics and Computing Architecture," IEEE Micro, vol. 28, no. 2, 2008, pp. 39–55.
3. J. Nickolls et al., "Scalable Parallel Programming with CUDA," Queue, vol. 6, no. 2, 2008, pp. 40–53.
4. J.M. Cohen and M.J. Molemaker, A Fast Double Precision CFD Code Using CUDA, tech. report NVR-2009-001, Nvidia, May 2009.
5. N. Bell and M. Garland, Efficient Sparse Matrix-Vector Multiplication on CUDA, tech. report NVR-2008-004, Nvidia, Dec. 2008.
6. M. Garland et al., "Parallel Computing Experiences with CUDA," IEEE Micro, vol. 28, no. 4, 2008, pp. 13–27.
7. J.D. Owens et al., "GPU Computing," Proc. IEEE, vol. 96, no. 5, 2008, pp. 879–899.
16 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool