publication . Article . Other literature type . Preprint . 2009

PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation

Klöckner, Andreas; Pinto, Nicolas; Lee, Yunsup; Catanzaro, Bryan; Ivanov, Paul; Fasih, Ahmed;
Open Access
  • Published: 17 Nov 2009 Journal: Parallel Computing, volume 38, pages 157-174 (issn: 0167-8191, Copyright policy)
  • Publisher: Elsevier BV
Abstract
Comment: Submitted to Parallel Computing, Elsevier
Subjects
free text keywords: Theoretical Computer Science, Computer Networks and Communications, Hardware and Architecture, Software, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Scripting language, computer.software_genre, computer, Code generation, SIMD, Massively parallel, High-level programming language, Computer science, Parallel computing, Python (programming language), computer.programming_language, CUDA, Graphics, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Software Engineering, D.1.2
56 references, page 1 of 4

[1] N. Bell and M. Garland. Implementing sparse matrix-vector multiplication on throughput-oriented processors. In SC '09: Proceedings of the 2009 ACM/IEEE conference on Supercomputing, New York, NY, USA, 2009. ACM.

[2] I. Buck, T. Foley, D. Horn, J. Sugerman, K. Fatahalian, M. Houston, and P. Hanrahan. Brook for GPUs: stream computing on graphics hardware. In Int. Conf. on Computer Graphics and Interactive Techniques, pages 777{786. ACM New York, NY, USA, 2004.

[3] B. Catanzaro, M. Garland, and K. Keutzer. Copperhead: Compiling an embedded data parallel language. In Principles and Practices of Parallel Programming (PPoPP), pages 47{56, 2011. [OpenAIRE]

[4] D. M. Chandler and D. J. Field. Estimates of the information content and dimensionality of natural scenes from proximity distributions. J. Opt. Soc. Am A Opt. Image Sci. Vis., 24(4):922{941, Apr. 2007.

[5] L. Dalc n, R. Paz, and M. Storti. MPI for Python. J. Par. Dist. Comp., 65(9):1108{1115, Sept. 2005.

[6] W. J. Dally, P. Hanrahan, M. Erez, T. J. Knight, F. Labonte, J. H. Ahn, N. Jayasena, U. J. Kapasi, A. Das, and J. Gummaraju. Merrimac: Supercomputing with streams. In Proc. of the ACM/IEEE SC2003 Conference (SC'03), volume 1, 2003.

[7] J. de Guzman. The Boost Spirit Parser Generator Framework, 2008. sourceforge.net/.

[8] B. Eich. JavaScript at ten years. In Proceedings of the tenth ACM SIGPLAN international conference on Functional programming, page 129. ACM, 2005. ISBN 1595930647. URL http://ecmascript.org. [OpenAIRE]

[9] A. R. Fasih and T. D. R. Hartley. GPU-accelerated synthetic aperture radar backprojection in CUDA. In Radar Conference, 2010 IEEE, pages 1408{1413, May 2010. [OpenAIRE]

[10] J. Feldman and D. Gries. Translator writing systems. Commun. ACM, 11:77{113, February 1968. ISSN 0001-0782. doi: 10.1145/362896.362902. [OpenAIRE]

[11] D. Flanagan and Y. Matsumoto. The Ruby programming language. O'Reilly, 2008. URL http://www. ruby-lang.org.

[12] M. Frigo and S. G. Johnson. The design and implementation of FFTW3. Proc. IEEE, 93(2):216{231, 2005. Special issue on \Program Generation, Optimization, and Platform Adaptation".

[13] G. Fursin, C. Miranda, O. Temam, M. Namolaru, E. Yom-Tov, A. Zaks, B. Mendelson, P. Barnard, E. Ashton, E. Courtois, F. Bodin, E. Bonilla, J. Thomson, H. Leather, C. Williams, and M. O'Boyle. MILEPOST GCC: machine learning based research compiler. In Proc. GCC Developers' Summit, June 2008. [OpenAIRE]

[14] K. O. W. Group. The OpenCL 1.0 Speci cation. Khronos Group, Beaverton, OR, Dec. 2008.

[15] T. D. Han and T. S. Abdelrahman. /hi/cuda: a high-level directive-based language for gpu programming. In GPGPU-2: Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units, pages 52{61, New York, NY, USA, 2009. ACM. ISBN 978-1-60558-517-8. doi: http://doi.acm.org/10.1145/1513895.1513902. [OpenAIRE]

56 references, page 1 of 4
Abstract
Comment: Submitted to Parallel Computing, Elsevier
Subjects
free text keywords: Theoretical Computer Science, Computer Networks and Communications, Hardware and Architecture, Software, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Scripting language, computer.software_genre, computer, Code generation, SIMD, Massively parallel, High-level programming language, Computer science, Parallel computing, Python (programming language), computer.programming_language, CUDA, Graphics, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Software Engineering, D.1.2
56 references, page 1 of 4

[1] N. Bell and M. Garland. Implementing sparse matrix-vector multiplication on throughput-oriented processors. In SC '09: Proceedings of the 2009 ACM/IEEE conference on Supercomputing, New York, NY, USA, 2009. ACM.

[2] I. Buck, T. Foley, D. Horn, J. Sugerman, K. Fatahalian, M. Houston, and P. Hanrahan. Brook for GPUs: stream computing on graphics hardware. In Int. Conf. on Computer Graphics and Interactive Techniques, pages 777{786. ACM New York, NY, USA, 2004.

[3] B. Catanzaro, M. Garland, and K. Keutzer. Copperhead: Compiling an embedded data parallel language. In Principles and Practices of Parallel Programming (PPoPP), pages 47{56, 2011. [OpenAIRE]

[4] D. M. Chandler and D. J. Field. Estimates of the information content and dimensionality of natural scenes from proximity distributions. J. Opt. Soc. Am A Opt. Image Sci. Vis., 24(4):922{941, Apr. 2007.

[5] L. Dalc n, R. Paz, and M. Storti. MPI for Python. J. Par. Dist. Comp., 65(9):1108{1115, Sept. 2005.

[6] W. J. Dally, P. Hanrahan, M. Erez, T. J. Knight, F. Labonte, J. H. Ahn, N. Jayasena, U. J. Kapasi, A. Das, and J. Gummaraju. Merrimac: Supercomputing with streams. In Proc. of the ACM/IEEE SC2003 Conference (SC'03), volume 1, 2003.

[7] J. de Guzman. The Boost Spirit Parser Generator Framework, 2008. sourceforge.net/.

[8] B. Eich. JavaScript at ten years. In Proceedings of the tenth ACM SIGPLAN international conference on Functional programming, page 129. ACM, 2005. ISBN 1595930647. URL http://ecmascript.org. [OpenAIRE]

[9] A. R. Fasih and T. D. R. Hartley. GPU-accelerated synthetic aperture radar backprojection in CUDA. In Radar Conference, 2010 IEEE, pages 1408{1413, May 2010. [OpenAIRE]

[10] J. Feldman and D. Gries. Translator writing systems. Commun. ACM, 11:77{113, February 1968. ISSN 0001-0782. doi: 10.1145/362896.362902. [OpenAIRE]

[11] D. Flanagan and Y. Matsumoto. The Ruby programming language. O'Reilly, 2008. URL http://www. ruby-lang.org.

[12] M. Frigo and S. G. Johnson. The design and implementation of FFTW3. Proc. IEEE, 93(2):216{231, 2005. Special issue on \Program Generation, Optimization, and Platform Adaptation".

[13] G. Fursin, C. Miranda, O. Temam, M. Namolaru, E. Yom-Tov, A. Zaks, B. Mendelson, P. Barnard, E. Ashton, E. Courtois, F. Bodin, E. Bonilla, J. Thomson, H. Leather, C. Williams, and M. O'Boyle. MILEPOST GCC: machine learning based research compiler. In Proc. GCC Developers' Summit, June 2008. [OpenAIRE]

[14] K. O. W. Group. The OpenCL 1.0 Speci cation. Khronos Group, Beaverton, OR, Dec. 2008.

[15] T. D. Han and T. S. Abdelrahman. /hi/cuda: a high-level directive-based language for gpu programming. In GPGPU-2: Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units, pages 52{61, New York, NY, USA, 2009. ACM. ISBN 978-1-60558-517-8. doi: http://doi.acm.org/10.1145/1513895.1513902. [OpenAIRE]

56 references, page 1 of 4
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publication . Article . Other literature type . Preprint . 2009

PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation

Klöckner, Andreas; Pinto, Nicolas; Lee, Yunsup; Catanzaro, Bryan; Ivanov, Paul; Fasih, Ahmed;