CUDA

The NVIDIA® CUDA® Toolkit provides a comprehensive development environment for C and C++ developers building GPU-accelerated applications. The CUDA Toolkit includes a compiler for NVIDIA GPUs, math libraries, and tools for debugging and optimizing the performance of your applications. You’ll also find programming guides, user manuals, API reference, and other documentation to help you get started quickly accelerating your application with GPUs.


References in zbMATH (referenced in 790 articles , 2 standard articles )

Showing results 1 to 20 of 790.
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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. Aissa, Mohamed; Verstraete, Tom; Vuik, Cornelis: Toward a GPU-aware comparison of explicit and implicit CFD simulations on structured meshes (2017)
  3. Alonso, Pedro; Ibáñez, Javier; Sastre, Jorge; Peinado, Jesús; Defez, Emilio: Efficient and accurate algorithms for computing matrix trigonometric functions (2017)
  4. Amaral, Sergio; Allaire, Douglas; Willcox, Karen: Optimal $L_2$-norm empirical importance weights for the change of probability measure (2017)
  5. Antti-Pekka Hynninen, Dmitry I. Lyakh: cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs (2017) arXiv
  6. Avrachenkov, K.; Chebotarev, P.; Mishenin, A.: Semi-supervised learning with regularized Laplacian (2017)
  7. Boubekeur, Mohamed; Benkhaldoun, Fayssal; Seaid, Mohammed: GPU accelerated finite volume methods for three-dimensional shallow water flows (2017)
  8. Cedric Nugteren: CLBlast: A Tuned OpenCL BLAS Library (2017) arXiv
  9. Chen, Cheng; Fang, Jianbin; Tang, Tao; Yang, Canqun: LU factorization on heterogeneous systems: an energy-efficient approach towards high performance (2017)
  10. Chen, Jingmin; Grundel, Sara; Yu, Thomas P.Y.: A flexible $C^2$ subdivision scheme on the sphere: with application to biomembrane modelling (2017)
  11. Chen, Tianran; Lee, Tsung-Lin; Li, Tien-Yien: Mixed cell computation in HOM4ps (2017)
  12. Chen, Tianran; Mehta, Dhagash: Parallel degree computation for binomial systems (2017)
  13. Conte, Dajana; Paternoster, Beatrice: Parallel methods for weakly singular Volterra integral equations on GPUs (2017)
  14. de Paula, Lauro Cássio Martins; Soares, Anderson S.; Soares, Telma W.L.; Filho, Arlindo R.G.; Coelho, Clarimar J.; Delbem, Alexandre C.B.; Martins, Wellington S.: Parallel regressions for variable selection using GPU (2017)
  15. Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
  16. Hanyu Jiang, Narayan Ganesan, Yu-Dong Yao: CUDAMPF++: A Proactive Resource Exhaustion Scheme for Accelerating Homologous Sequence Search on CUDA-enabled GPU (2017) arXiv
  17. Ingo Steinwart, Philipp Thomann: liquidSVM: A Fast and Versatile SVM package (2017) arXiv
  18. 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)
  19. Kojima, Kensuke; Igarashi, Atsushi: A Hoare logic for GPU kernels (2017)
  20. Lefticaru, Raluca; Macías-Ramos, Luis F.; Niculescu, Ionuţ Mihai; Mierlă, Laurenţiu: Agent-based simulation of kernel P systems with division rules using FLAME (2017)

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