The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation
Open accessAbstract
High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that supports this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success.
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Methods of conjugate gradients for solving linear systems
Hestenes, M. R., & Stiefel, E. (1952) · Journal of Research of the National Bureau of Standards · doi:10.6028/jres.049.044 · Cited by 8,132
The Design and Implementation of FFTW3
Frigo, M., & Johnson, S. G. (2005) · Proceedings of the IEEE · doi:10.1109/jproc.2004.840301 · Cited by 5,086
A Bridging Model for Parallel Computation
Valiant, L. G. (1990) · ACM eBooks · doi:10.1145/3749740.3749753 · Cited by 3,664
A Survey of General‐Purpose Computation on Graphics Hardware
Owens, J. D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A. E., & Purcell, T. J. (2007) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2007.01012.x · Cited by 2,101
Synthesis and evaluation of linear motion transitions
Wang, J., & Bodenheimer, B. (2008) · ACM Transactions on Graphics · doi:10.1145/1330511.1330512 · Cited by 1,534
NVIDIA Tesla: A Unified Graphics and Computing Architecture
Lindholm, E., Nickolls, J., Oberman, S., & Montrym, J. (2008) · IEEE Micro · doi:10.1109/mm.2008.31 · Cited by 1,362
Automated empirical optimizations of software and the ATLAS project
Clint Whaley, R., Petitet, A., & Dongarra, J. J. (2001) · Parallel Computing · doi:10.1016/s0167-8191(00)00087-9 · Cited by 1,317
Brook for GPUs: stream computing on graphics hardware
Buck, I., Foley, T., Horn, D., Sugerman, J., Fatahalian, K., Houston, M., & Hanrahan, P. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015800 · Cited by 1,155
Independent component filters of natural images compared with simple cells in primary visual cortex
van Hateren, J. H., & van der Schaaf, A. (1998) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rspb.1998.0303 · Cited by 1,106
Optimizing Compilers for Modern Architectures: A Dependence-based Approach
Kennedy, K., & Allen, J. R. (2001) · Cited by 1,024
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Metadata
- Authors
- Klöckner, A., Pinto, N., Lee, Y., Catanzaro, B., Ivanov, P., & Fasih, A.
- Venue
- Parallel Computing
- Publisher
- Elsevier BV
- Published
- 2012-03
- Volume
- 38
- Issue
- 3
- Pages
- 157-174
- ISSN
- 0167-8191 (ISSN-L)
- OpenAlex
- W1985462363
- Cited by
- 573
- References
- 25
- FWCI
- 50.23
- Type
- journal article
- Language
- en
- Open access
- green
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- Klöckner, A., Pinto, N., Lee, Y., Catanzaro, B., Ivanov, P., & Fasih, A. (2012). PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation. Parallel Computing. https://doi.org/10.1016/j.parco.2011.09.001
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