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.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 216 articles )

Showing results 1 to 20 of 216.
Sorted by year (citations)

1 2 3 ... 9 10 11 next

  1. Algosaibi, Abdulelah A.: High-performance computing based approach for improving semantic-based federated data processing (2021)
  2. Avramidis, Eleftherios; Lalik, Marta; Akman, Ozgur E.: SODECL: an open-source library for calculating multiple orbits of a system of stochastic differential equations in parallel (2020)
  3. Reguly, István Z.; Mudalige, Gihan R.: Productivity, performance, and portability for computational fluid dynamics applications (2020)
  4. Chen, Yewang; Zhou, Lida; Tang, Yi; Singh, Jai Puneet; Bouguila, Nizar; Wang, Cheng; Wang, Huazhen; Du, Jixiang: Fast neighbor search by using revised (k)-d tree (2019)
  5. Chopp, D. L.: Introduction to high performance scientific computing (2019)
  6. Demidov, D.: AMGCL: an efficient, flexible, and extensible algebraic multigrid implementation (2019)
  7. Erofeev, K. Yu.; Khramchenkov, E. M.; Biryal’tsev, E. V.: High-performance processing of covariance matrices using GPU computations (2019)
  8. Faict, Thomas; D’Hollander, Erik H.; Goossens, Bart: Mapping a guided image filter on the HARP reconfigurable architecture using OpenCL (2019)
  9. Gadioli, Davide; Vitali, Emanuele; Palermo, Gianluca; Silvano, Cristina: mARGOt: a dynamic autotuning framework for self-aware approximate computing (2019)
  10. Goldenberg, Steven; Stathopoulos, Andreas; Romero, Eloy: A Golub-Kahan Davidson method for accurately computing a few singular triplets of large sparse matrices (2019)
  11. Mingyuan Wu, Husheng Zhou, Lingming Zhang, Cong Liu, Yuqun Zhang: Charactering and Detecting CUDA Program Bugs (2019) arXiv
  12. Mossaiby, Farshid; Joulaian, Meysam; Düster, Alexander: The spectral cell method for wave propagation in heterogeneous materials simulated on multiple GPUs and CPUs (2019)
  13. Terenin, Alexander; Dong, Shawfeng; Draper, David: GPU-accelerated Gibbs sampling: a case study of the horseshoe probit model (2019)
  14. 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)
  15. Cavoretto, Roberto; Schneider, Teseo; Zulian, Patrick: \textscOpenCLbased parallel algorithm for RBF-PUM interpolation (2018)
  16. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  17. Gorobets, A.; Soukov, S.; Bogdanov, P.: Multilevel parallelization for simulating compressible turbulent flows on most kinds of hybrid supercomputers (2018)
  18. 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)
  19. Robinson, Jeffrey A.; Vrbsky, Susan V.; Hong, Xiaoyan; Eddy, Brian P.: Analysis of a high-performance TSP solver on the GPU (2018)
  20. Shen, Li; Quinto, Eric Todd; Wang, Shiqiang; Jiang, Ming: Simultaneous reconstruction and segmentation with the Mumford-Shah functional for electron tomography (2018)

1 2 3 ... 9 10 11 next