GPU ACCELERATION OF NUMERICAL WEATHER PREDICTION
Abstract
Weather and climate prediction software has enjoyed the benefits of exponentially increasing processor power for almost 50 years. Even with the advent of large-scale parallelism in weather models, much of the performance increase has come from increasing processor speed rather than increased parallelism. This free ride is nearly over. Recent results also indicate that simply increasing the use of large-scale parallelism will prove ineffective for many scenarios where strong scaling is required. We present an alternative method of scaling model performance by exploiting emerging architectures using the fine-grain parallelism once used in vector machines. The paper shows the promise of this approach by demonstrating a nearly 10 × speedup for a computationally intensive portion of the Weather Research and Forecast (WRF) model on a variety of NVIDIA Graphics Processing Units (GPU). This change alone speeds up the whole weather model by 1.23×.
References
- Technical Brief: NVIDIA GeForce 8800 GPU Architecture Overview. Santa Clara, California, November 2006 . Google Scholar
- NVIDIA CUDA Compute Unified Device Architecture: Programming Guide Version 0.8. Santa Clara, California, February 2007 . Google Scholar
- CUDA Profiler Documentation (distributed with SDK 2.0 beta). Santa Clara, California, June 2008 . Google Scholar
- K. Asanovic, R. Bodik, B. C. Catanzaro, J. J. Gebis, P. Husbands, K. Keutzer, D. A. Patterson, W. L. Plishker, J. Shalf, S. W. Williams, and K. A. Yelick. The landscape of parallel computing research: A view from Berkeley. Technical Report UCB/EECS-2006-183, EECS Department, University of California, Berkeley, Dec 2006 . Google Scholar
- I. Buck. Stream Computing for Graphics Hardware. PhD thesis, Department of Computer Science, Stanford University, Palo Alto, California, United States, Sept. 2006 . Google Scholar
- Atmos. Ocean. Tech. 10, 195 (1993). Crossref, ISI, Google Scholar
- Parallel Computing 21, 1593 (1995), DOI: 10.1016/0167-8191(95)01017-9. Crossref, ISI, Google Scholar
-
B. Himawan and M. Vachharajani , Deconstructing Hardware Usage for General Purpose Computation on GPUs , Fifth Annual Workshop on Duplicating, Deconstructing, and Debunking (in conjunction with ISCA-33) ( 2006 ) . Google Scholar - Monthly Weather Review 132(1), 103. Crossref, ISI, Google Scholar
J. Michalakes , Wrf nature run, Proceedings of the 2007 ACM/IEEE conference on Supercomputing (2007) pp. 1–6. Google ScholarJ. Michalakes and M. Vachharajani , Gpu acceleration of numerical weather prediction, Proceedings of the workshop on Large Scale Parallel Processing (LSPP) in the IEEE International Parallel and Distributed Processing Symposium (2008) pp. 1–8. Google ScholarS. Shingu , A 26.58 tflops global atmospheric simulation with the spectral transform method on the earth simulator, Proceedings of the 2002 ACM/IEEE conference on Supercomputing (IEEE Computer Society Press, 2002) pp. 1–19. Google Scholar- W. C. Skamarock, J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers. A description of the advanced research WRF version 2. Technical Report NCAR/TN-468+STR, National Center for Atmospheric Research, Jan. 2007 . Google Scholar


