PyOpenCL gives you easy, Pythonic access to the OpenCL parallel computation API. Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. Completeness. PyOpenCL puts the full power of OpenCL’s API at your disposal, if you wish. Every obscure get_info() query and all CL calls are accessible. Automatic Error Checking. All errors are automatically translated into Python exceptions. Speed. PyOpenCL’s base layer is written in C++, so all the niceties above are virtually free. Helpful Documentation. You’re looking at it. ;) Liberal license. PyOpenCL is open-source under the MIT license and free for commercial, academic, and private use.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Merrison-Hort, Robert: Fireflies: new software for interactively exploring dynamical systems using GPU computing (2015)
- Cottet, G.-H.; Etancelin, J.-M.; Perignon, F.; Picard, C.: High order semi-Lagrangian particle methods for transport equations: numerical analysis and implementation issues (2014)
- Januszewski, M.; Kostur, M.: Sailfish: a flexible multi-GPU implementation of the lattice Boltzmann method (2014)
- Witherden, F.D.; Farrington, A.M.; Vincent, P.E.: PyFR: an open source framework for solving advection-diffusion type problems on streaming architectures using the flux reconstruction approach (2014)
- Klöckner, Andreas; Pinto, Nicolas; Lee, Yunsup; Catanzaro, Bryan; Ivanov, Paul; Fasih, Ahmed: PyCUDA and PyOpenCL: a scripting-based approach to GPU run-time code generation (2012)
- Lazar, Aurel A.; Zhou, Yiyin: Massively parallel neural encoding and decoding of visual stimuli (2012)
- Strzodka, Robert: Data layout optimization for multi-valued containers in opencl (2012)