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 891 articles , 2 standard articles )

Showing results 1 to 20 of 891.
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  1. Cavoretto, Roberto; Schneider, Teseo; Zulian, Patrick: OpenCL based parallel algorithm for RBF-PUM interpolation (2018)
  2. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  3. D’Amore, Luisa; Romano, Diego: An objective criterion for stopping light-surface interaction. Numerical validation and quality assessment (2018)
  4. Defez, Emilio; Ibáñez, Javier; Sastre, Jorge; Peinado, Jesús; Alonso, Pedro: A new efficient and accurate spline algorithm for the matrix exponential computation (2018)
  5. Dimarco, Giacomo; Loubère, Raphaël; Narski, Jacek; Rey, Thomas: An efficient numerical method for solving the Boltzmann equation in multidimensions (2018)
  6. Fioretto, Ferdinando; Pontelli, Enrico; Yeoh, William; Dechter, Rina: Accelerating exact and approximate inference for (distributed) discrete optimization with GPUs (2018)
  7. Graf, Jonathan S.; Gobbert, Matthias K.; Khuvis, Samuel: Long-time simulations with complex code using multiple nodes of Intel Xeon Phi knights landing (2018)
  8. Grzybowski, J.M.V.; Macau, Elbert E.N.; Yoneyama, Takashi: Power-grids as complex networks: emerging investigations into robustness and stability (2018)
  9. Kojima, Kensuke; Imanishi, Akifumi; Igarashi, Atsushi: Automated verification of functional correctness of race-free GPU programs (2018)
  10. Magee, Daniel J.; Niemeyer, Kyle E.: Accelerating solutions of one-dimensional unsteady PDEs with GPU-based swept time-space decomposition (2018)
  11. Shabat, Gil; Shmueli, Yaniv; Aizenbud, Yariv; Averbuch, Amir: Randomized LU decomposition (2018)
  12. Yang, Wangdong; Li, Kenli; Li, Keqin: A parallel computing method using blocked format with optimal partitioning for SpMV on GPU (2018)
  13. Yianni, Panayioti C.; Neves, Luis C.; Rama, Dovile; Andrews, John D.: Accelerating Petri-net simulations using NVIDIA graphics processing units (2018)
  14. Zhao, Yan; Chen, Liping; Xie, Gang; Zhao, Jianjun; Ding, Jianwan: GPU implementation of a cellular genetic algorithm for scheduling dependent tasks of physical system simulation programs (2018)
  15. Afzal, Asif; Ansari, Zahid; Rimaz Faizabadi, Ahmed; Ramis, M.K.: Parallelization strategies for computational fluid dynamics software: state of the art review (2017)
  16. Aissa, Mohamed; Verstraete, Tom; Vuik, Cornelis: Toward a GPU-aware comparison of explicit and implicit CFD simulations on structured meshes (2017)
  17. Alonso, Pedro; Ibáñez, Javier; Sastre, Jorge; Peinado, Jesús; Defez, Emilio: Efficient and accurate algorithms for computing matrix trigonometric functions (2017)
  18. Al-Refaie, Ahmed F.; Yurchenko, Sergei N.; Tennyson, Jonathan: GPU accelerated intensities MPI (GAIN-MPI): a new method of computing Einstein-$A$ coefficients (2017)
  19. Amaral, Sergio; Allaire, Douglas; Willcox, Karen: Optimal $L_2$-norm empirical importance weights for the change of probability measure (2017)
  20. Antti-Pekka Hynninen, Dmitry I. Lyakh: cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs (2017) arXiv

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