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 212 articles )

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  1. Reguly, István Z.; Mudalige, Gihan R.: Productivity, performance, and portability for computational fluid dynamics applications (2020)
  2. Chopp, D. L.: Introduction to high performance scientific computing (2019)
  3. Demidov, D.: AMGCL: an efficient, flexible, and extensible algebraic multigrid implementation (2019)
  4. Erofeev, K. Yu.; Khramchenkov, E. M.; Biryal’tsev, E. V.: High-performance processing of covariance matrices using GPU computations (2019)
  5. Gadioli, Davide; Vitali, Emanuele; Palermo, Gianluca; Silvano, Cristina: mARGOt: a dynamic autotuning framework for self-aware approximate computing (2019)
  6. Goldenberg, Steven; Stathopoulos, Andreas; Romero, Eloy: A Golub-Kahan Davidson method for accurately computing a few singular triplets of large sparse matrices (2019)
  7. Mingyuan Wu, Husheng Zhou, Lingming Zhang, Cong Liu, Yuqun Zhang: Charactering and Detecting CUDA Program Bugs (2019) arXiv
  8. Mossaiby, Farshid; Joulaian, Meysam; Düster, Alexander: The spectral cell method for wave propagation in heterogeneous materials simulated on multiple GPUs and CPUs (2019)
  9. Terenin, Alexander; Dong, Shawfeng; Draper, David: GPU-accelerated Gibbs sampling: a case study of the horseshoe probit model (2019)
  10. Álvarez, X.; Gorobets, A.; Trias, F. X.; Borrell, R.; Oyarzun, G.: HPC(^2) -- a fully-portable, algebra-based framework for heterogeneous computing. Application to CFD (2018)
  11. Cavoretto, Roberto; Schneider, Teseo; Zulian, Patrick: \textscOpenCLbased parallel algorithm for RBF-PUM interpolation (2018)
  12. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  13. Gorobets, A.; Soukov, S.; Bogdanov, P.: Multilevel parallelization for simulating compressible turbulent flows on most kinds of hybrid supercomputers (2018)
  14. Pikle, Nileshchandra K.; Sathe, Shailesh R.; Vyavhare, Arvind Y.: GPGPU-based parallel computing applied in the FEM using the conjugate gradient algorithm: a review (2018)
  15. Robinson, Jeffrey A.; Vrbsky, Susan V.; Hong, Xiaoyan; Eddy, Brian P.: Analysis of a high-performance TSP solver on the GPU (2018)
  16. Shen, Li; Quinto, Eric Todd; Wang, Shiqiang; Jiang, Ming: Simultaneous reconstruction and segmentation with the Mumford-Shah functional for electron tomography (2018)
  17. Vanderbauwhede, Wim; Davidson, Gavin: Domain-specific acceleration and auto-parallelization of legacy scientific code in FORTRAN 77 using source-to-source compilation (2018)
  18. Zhang, Jia-Le; Ma, Zhi-Hua; Chen, Hong-Quan; Cao, Cheng: A GPU-accelerated implicit meshless method for compressible flows (2018)
  19. Afzal, Asif; Ansari, Zahid; Rimaz Faizabadi, Ahmed; Ramis, M. K.: Parallelization strategies for computational fluid dynamics software: state of the art review (2017)
  20. Cedric Nugteren: CLBlast: A Tuned OpenCL BLAS Library (2017) arXiv

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