OpenCL

OpenCL™ is the first open, royalty-free standard for cross-platform, parallel programming of modern processors found in personal computers, servers and handheld/embedded devices. OpenCL (Open Computing Language) greatly improves speed and responsiveness for a wide spectrum of applications in numerous market categories from gaming and entertainment to scientific and medical software.


References in zbMATH (referenced in 156 articles )

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  1. Afzal, Asif; Ansari, Zahid; Rimaz Faizabadi, Ahmed; Ramis, M.K.: Parallelization strategies for computational fluid dynamics software: state of the art review (2017)
  2. Cedric Nugteren: CLBlast: A Tuned OpenCL BLAS Library (2017) arXiv
  3. Julián-Moreno, Guillermo; López de Vergara, Jorge E.; González, Iván; de Pedro, Luis; Royuela-del-Val, Javier; Simmross-Wattenberg, Federico: Fast parallel $\alpha $-stable distribution function evaluation and parameter estimation using OpenCL in GPGPUs (2017)
  4. Kojima, Kensuke; Igarashi, Atsushi: A Hoare logic for GPU kernels (2017)
  5. Lefticaru, Raluca; Macías-Ramos, Luis F.; Niculescu, Ionuţ Mihai; Mierlă, Laurenţiu: Agent-based simulation of kernel P systems with division rules using FLAME (2017)
  6. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  7. Peter Steinbach, Matthias Werner: gearshifft - The FFT Benchmark Suite for Heterogeneous Platforms (2017) arXiv
  8. Tingelstad, Lars; Egeland, Olav: Automatic multivector differentiation and optimization (2017)
  9. Toth, Csaba D. (ed.); Goodman, Jacob E. (ed.); O’Rourke, Joseph (ed.): Handbook of discrete and computational geometry (2017)
  10. Wickerson, John; Batty, Mark; Sorensen, Tyler; Constantinides, George A.: Automatically comparing memory consistency models (2017)
  11. Abdelfattah, Ahmad; Keyes, David; Ltaief, Hatem: KBLAS: an optimized library for dense matrix-vector multiplication on GPU accelerators (2016)
  12. Andrejić, Vladica; Tatarevic, Milos: Searching for a counterexample to Kurepa’s conjecture (2016)
  13. Bock, Nicolas; Challacombe, Matt; Kalé, Laxmikant V.: Solvers for $\mathcalO(N)$ electronic structure in the strong scaling limit (2016)
  14. Karakus, A.; Warburton, T.; Aksel, M.H.; Sert, C.: A GPU accelerated level set reinitialization for an adaptive discontinuous Galerkin method (2016)
  15. Mantas, José Miguel; de la Asunción, Marc; Castro, Manuel J.: An introduction to GPU computing for numerical simulation (2016) ioport
  16. Matthew Moskewicz, Forrest Iandola, Kurt Keutzer: Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms (2016) arXiv
  17. Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Scientific computations on multi-core systems using different programming frameworks (2016)
  18. Riesinger, Christoph; Neckel, Tobias; Rupp, Florian: Solving random ordinary differential equations on GPU clusters using multiple levels of parallelism (2016)
  19. Rupp, Karl; Tillet, Philippe; Rudolf, Florian; Weinbub, Josef; Morhammer, Andreas; Grasser, Tibor; Jüngel, Ansgar; Selberherr, Siegfried: ViennaCL-linear algebra library for multi- and many-core architectures (2016)
  20. Rupp, Karl; Weinbub, Josef; Jüngel, Ansgar; Grasser, Tibor: Pipelined iterative solvers with kernel fusion for graphics processing units (2016)

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