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 1068 articles , 2 standard articles )

Showing results 1 to 20 of 1068.
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  1. Acer, Seher; Kayaaslan, Enver; Aykanat, Cevdet: A hypergraph partitioning model for profile minimization (2019)
  2. Berrone, S.; D’Auria, A.; Vicini, F.: Fast and robust flow simulations in discrete fracture networks with gpgpus (2019)
  3. Berrone, S.; Scialò, S.; Vicini, F.: Parallel meshing, discretization, and computation of flow in massive discrete fracture networks (2019)
  4. Chen, Xiang; Wan, Decheng: Numerical simulation of three-dimensional violent free surface flows by GPU-based MPS method (2019)
  5. Chien, Yu-Tse; Hwang, Feng-Nan: A Markov chain-based multi-elimination preconditioner for elliptic PDE problems (2019)
  6. Chopp, D. L.: Introduction to high performance scientific computing (2019)
  7. 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
  8. 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)
  9. Erofeev, K. Yu.; Khramchenkov, E. M.; Biryal’tsev, E. V.: High-performance processing of covariance matrices using GPU computations (2019)
  10. He, Jiandong; Lei, Juanmian: A GPU-accelerated TLSPH algorithm for 3D geometrical nonlinear structural analysis (2019)
  11. Li, Ruipeng; Xi, Yuanzhe; Erlandson, Lucas; Saad, Yousef: The eigenvalues slicing library (EVSL): algorithms, implementation, and software (2019)
  12. Liu, Rex Kuan-Shuo; Wu, Cheng-Tao; Kao, Neo Shih-Chao; Sheu, Tony Wen-Hann: An improved mixed Lagrangian-Eulerian (IMLE) method for modelling incompressible Navier-Stokes flows with CUDA programming on multi-gpus (2019)
  13. Małysiak-Mrozek, Bożena: Uncertainty, imprecision, and many-valued logics in protein bioinformatics (2019)
  14. Mingyuan Wu, Husheng Zhou, Lingming Zhang, Cong Liu, Yuqun Zhang: Charactering and Detecting CUDA Program Bugs (2019) arXiv
  15. Mossaiby, Farshid; Joulaian, Meysam; Düster, Alexander: The spectral cell method for wave propagation in heterogeneous materials simulated on multiple GPUs and CPUs (2019)
  16. Sun, Jian-Qiao; Xiong, Fu-Rui; Schütze, Oliver; Hernández, Carlos: Cell mapping methods. Algorithmic approaches and applications (2019)
  17. van den Berg, E.: The Ocean Tensor Package (2019) not zbMATH
  18. Abbas-Turki, Lokman A.; Crépey, Stéphane; Diallo, Babacar: XVA principles, nested Monte Carlo strategies, and GPU optimizations (2018)
  19. Akhtar, Muhammad Naveed; Durad, Muhammad Hanif; Usman, Anila; Mughal, Muhammad Abid: Efficient memory access patterns for solving 3D Laplace equation on GPU (2018)
  20. Alimirzazadeh, Siamak; Jahanbakhsh, Ebrahim; Maertens, Audrey; Leguizamón, Sebastián; Avellan, François: GPU-accelerated 3-D finite volume particle method (2018)

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