Paulius Micikevicius
ABSTRACT
In this paper we describe a GPU parallelization of the 3D finite difference computation using CUDA. Data access redundancy is used as the metric to determine the optimal implementation for both the stencil-only computation, as well as the discretization of the wave equation, which is currently of great interest in seismic computing. For the larger stencils, the described approach achieves the throughput of between 2,400 to over 3,000 million of output points per second on a single Tesla 10-series GPU. This is roughly an order of magnitude higher than a 4-core Harpertown CPU running a similar code from seismic industry. Multi-GPU parallelization is also described, achieving linear scaling with GPUs by overlapping inter-GPU communication with computation.
- Baysal, E., Kosloff, D. D., and Sherwood, J. W. C. 1983. Reverse-time migration. Geophysics, 48, 1514--1524.Google Scholar
Cross Ref
- CUDA Programming Guide, 2.1, NVIDIA. http://developer.download.nvidia.com/compute/cuda/2_1/too lkit/docs/NVIDIA_CUDA_Programming_Guide_2.1.pdfGoogle Scholar
- Datta, K., Murphy, M., Volkov, V., Williams, S., Carter, J., Oliker, L., Patterson, D., Shalf, J., and Yelick, K. 2008. Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures. In Proceedings of the 2008 ACM/IEEE Conference on Supercomputing (Austin, Texas, November 15--21, 2008). Conference on High Performance Networking and Computing. IEEE Press, Piscataway, NJ, 1--12. Google ScholarDigital Library
- Kamil, S., Datta, K., Williams, S., Oliker, L., Shalf, J., and Yelick, K. 2006. Implicit and explicit optimizations for stencil computations. In Proceedings of the 2006 Workshop on Memory System Performance and Correctness (San Jose, California, October 22--22, 2006). MSPC '06. ACM, New York, NY, 51--60. Google ScholarDigital Library
- Lindholm, E., Nickolls, J., Oberman, S., Montrym, J. 2008. NVIDIA Tesla: A Unified Graphics and Computing Architecture. IEEE Micro 28, 2 (Mar. 2008), 39--55. Google ScholarDigital Library
- McMechan, G. A. 1983. Migration by extrapolation of time-dependent boundary values. Geophys. Prosp., 31, 413--420.Google ScholarCross Ref
- Nickolls, J., Buck, I., Garland, M., and Skadron, K. 2008. Scalable Parallel Programming with CUDA. Queue 6, 2 (Mar. 2008), 40--53. Google ScholarDigital Library
Index Terms
- 3D finite difference computation on GPUs using CUDA
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