Akira Nukada
ABSTRACT
Fast Fourier transform is one of the most important computations used in many kinds of applications. Although there are several works of on single GPU FFT, we also need large-scale transforms that require multiple GPUs due to the capacity of the device memory. We present high performance 3-D FFT using multiple GPU devices both on a single node and on multiple nodes. As a result of optimizing the data transfer between GPUs, our multi GPU FFT successfully outperform single GPU.
- J. W. Cooley and J. W. Tukey. An Algorithm for the Machine Calculation of Complex Fourier Series. Math. Comput., Vol. 19:297--301, 1965.Google Scholar
Cross Ref
- Y. Dotsenko, S. S. Baghsorkhi, B. Lloyd, and N. K. Govindaraju. Auto-tuning of fast Fourier transform on graphics processors. In Proceedings of the 16th ACM symposium on Principles and practice of parallel programming, PPoPP '11, pages 257--266, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- General-Purpose Computation Using Graphics Hardware. http://www.gpgpu.org/.Google Scholar
- N. K. Govindaraju, B. Lloyd, Y. Dotsenko, B. Smith, and J. Manferdelli. High Performance Discrete Fourier Transforms on Graphics Processors. In the 2008 ACM/IEEE conference on supercomputing, 2008. Google ScholarDigital Library
- F. Hirata. Molecular theory of solvation. Dordrecht, The Netherlands, 2003.Google Scholar
- T. Imai, A. Kovalenko, F. Hirata, and A. Kidera. A new approach for investigating the molecular recognition of protein: Toward structure-based drug design based on the 3D-RISM theory. J. Am. Chem. Soc., 131:12430--12440, 2009.Google ScholarCross Ref
- Y. Kiyota, N. Yoshida, and F. Hirata. A new approach for investigating the molecular recognition of protein: Toward structure-based drug design based on the 3D-RISM theory. J. Comp. Theo. Chem., 7:3803--3815, 2011.Google ScholarCross Ref
- E. Lindholm, J. Nickolls, S. Oberman, and J. Montrym. NVIDIA Tesla: A Unified Graphics and Computing Architecture. IEEE Micro, 28(2):39--55, 2008. Google ScholarDigital Library
- K. Moreland and E. Angel. The FFT on a GPU. In Proceedings of SIGGRAPH/Eurographics Workshop on Graphics Hardware 2003, pages 112--119, 2003. Google ScholarDigital Library
- A. Nukada and S. Matsuoka. NukadaFFT: An auto-tuning FFT library for CUDA GPUs. In NVIDIA GPU Technology Conference 2010 (Research Summit Poster).Google Scholar
- A. Nukada and S. Matsuoka. Auto-tuning 3-D FFT library for CUDA GPUs. In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, SC '09, pages 30:1--30:10, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- A. Nukada, Y. Ogata, T. Endo, and S. Matsuoka. Bandwidth intensive 3-D FFT kernel for GPUs using CUDA. In SC '08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing, pages 1--11, Piscataway, NJ, USA, 2008. IEEE Press. Google ScholarDigital Library
- NVIDIA CUDA. Compute Unified Device Architecture. http://developer.nvidia.com/object/cuda.html.Google Scholar
- C. Van Loan. Computational Frameworks for the Fast Fourier Transform. SIAM Press, Philadelphia, PA, 1992. Google ScholarDigital Library
- V. Volkov and B. Kazian. Fitting FFT onto the G80 architecture, 2008. http://www.cs.berkeley.edu/~kubitron/courses/cs258-S08/projects/reports/project6_report.pdf.Google Scholar
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