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 1112 articles , 2 standard articles )

Showing results 1 to 20 of 1112.
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  1. Mena, Hermann; Pfurtscheller, Lena-Maria; Stillfjord, Tony: GPU acceleration of splitting schemes applied to differential matrix equations (2020)
  2. Nogueira, Bruno; Pinheiro, Rian G. S.: A GPU based local search algorithm for the unweighted and weighted maximum (s)-plex problems (2020)
  3. Reguly, István Z.; Mudalige, Gihan R.: Productivity, performance, and portability for computational fluid dynamics applications (2020)
  4. Žukovič, Milan; Borovský, Michal; Lach, Matúš; Hristopulos, Dionissios T.: GPU-accelerated simulation of massive spatial data based on the modified planar rotator model (2020)
  5. Acer, Seher; Kayaaslan, Enver; Aykanat, Cevdet: A hypergraph partitioning model for profile minimization (2019)
  6. Alpak, F. O.; Zacharoudiou, I.; Berg, S.; Dietderich, J.; Saxena, N.: Direct simulation of pore-scale two-phase visco-capillary flow on large digital rock images using a phase-field lattice Boltzmann method on general-purpose graphics processing units (2019)
  7. Bernaschi, Massimo; Carrozzo, Mauro; Franceschini, Andrea; Janna, Carlo: A dynamic pattern factored sparse approximate inverse preconditioner on graphics processing units (2019)
  8. Berrone, S.; D’Auria, A.; Vicini, F.: Fast and robust flow simulations in discrete fracture networks with gpgpus (2019)
  9. Berrone, S.; Scialò, S.; Vicini, F.: Parallel meshing, discretization, and computation of flow in massive discrete fracture networks (2019)
  10. Cheng, Xuan; Zeng, Ming; Lin, Jinpeng; Wu, Zizhao; Liu, Xinguo: Efficient (L_0) resampling of point sets (2019)
  11. Chen, Xiang; Wan, Decheng: Numerical simulation of three-dimensional violent free surface flows by GPU-based MPS method (2019)
  12. Chien, Yu-Tse; Hwang, Feng-Nan: A Markov chain-based multi-elimination preconditioner for elliptic PDE problems (2019)
  13. Chopp, D. L.: Introduction to high performance scientific computing (2019)
  14. Chow, Alex D.; Rogers, Benedict D.; Lind, Steven J.; Stansby, Peter K.: Numerical wave basin using incompressible smoothed particle hydrodynamics (ISPH) on a single GPU with vertical cylinder test cases (2019)
  15. Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viegas, Martin Wattenberg: TensorFlow.js: Machine Learning for the Web and Beyond (2019) arXiv
  16. Datta, Amitava; Kaur, Amardeep; Lauer, Tobias; Chabbouh, Sami: Exploiting multi-core and many-core parallelism for subspace clustering (2019)
  17. Defez, Emilio; Ibáñez, Javier; Peinado, Jesús; Sastre, Jorge; Alonso-Jordá, Pedro: An efficient and accurate algorithm for computing the matrix cosine based on new Hermite approximations (2019)
  18. Demidov, D.: AMGCL: an efficient, flexible, and extensible algebraic multigrid implementation (2019)
  19. Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
  20. Erofeev, K. Yu.; Khramchenkov, E. M.; Biryal’tsev, E. V.: High-performance processing of covariance matrices using GPU computations (2019)

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