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 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