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 582 articles , 2 standard articles )

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  1. Tranquilli, Paul; Glandon, S.Ross; Sarshar, Arash; Sandu, Adrian: Analytical Jacobian-vector products for the matrix-free time integration of partial differential equations (2017)
  2. Andersson, Fredrik; Carlsson, Marcus; Nikitin, Viktor V.: Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators (2016)
  3. Bernaschi, Massimo; Bisson, Mauro; Fantozzi, Carlo; Janna, Carlo: A factored sparse approximate inverse preconditioned conjugate gradient solver on graphics processing units (2016)
  4. Bialas, Piotr; Strzelecki, Adam: Benchmarking the cost of thread divergence in CUDA (2016)
  5. Bock, Nicolas; Challacombe, Matt; Kalé, Laxmikant V.: Solvers for $\mathcalO(N)$ electronic structure in the strong scaling limit (2016)
  6. Conte, Dajana; D’Ambrosio, Raffaele; Paternoster, Beatrice: GPU-acceleration of waveform relaxation methods for large differential systems (2016)
  7. Dai, Wei; Sunar, Berk: cuHE: a homomorphic encryption accelerator library (2016)
  8. D’Ambra, Pasqua; Filippone, Salvatore: A parallel generalized relaxation method for high-performance image segmentation on GPUs (2016)
  9. Joldes, Mioara; Muller, Jean-Michel; Popescu, Valentina; Tucker, Warwick: CAMPARY: cuda multiple precision arithmetic library and applications (2016)
  10. Kramer, Stephan C.; Hagemann, Johannes; Künneke, Lutz; Lebert, Jan: Parallel statistical multiresolution estimation for image reconstruction (2016)
  11. Mantas, José Miguel; de la Asunción, Marc; Castro, Manuel J.: An introduction to GPU computing for numerical simulation (2016)
  12. Matloff, Norman: Parallel computing for data science. With examples in R, C++ and CUDA (2016)
  13. Ma, Yan; Chen, Lajiao; Liu, Peng; Lu, Ke: Parallel programing templates for remote sensing image processing on GPU architectures: design and implementation (2016)
  14. Nguyen, Ken; Guo, Xuan; Pan, Yi: Multiple biological sequence alignment. Scoring functions, algorithms and evaluation (2016)
  15. O’Donoghue, Brendan; Chu, Eric; Parikh, Neal; Boyd, Stephen: Conic optimization via operator splitting and homogeneous self-dual embedding (2016)
  16. Reis, Ruy Freitas; Loureiro, Felipe dos Santos; Lobosco, Marcelo: 3D numerical simulations on GPUs of hyperthermia with nanoparticles by a nonlinear bioheat model (2016)
  17. Riesinger, Christoph; Neckel, Tobias; Rupp, Florian: Solving random ordinary differential equations on GPU clusters using multiple levels of parallelism (2016)
  18. Říha, Lubomír; Brzobohatý, Tomáš; Markopoulos, Alexandros; Kozubek, Tomáš; Meca, Ondřej; Schenk, Olaf; Vanroose, Wim: Efficient implementation of total FETI solver for graphic processing units using Schur complement (2016)
  19. Tasora, Alessandro; Serban, Radu; Mazhar, Hammad; Pazouki, Arman; Melanz, Daniel; Fleischmann, Jonathan; Taylor, Michael; Sugiyama, Hiroyuki; Negrut, Dan: Chrono: an open source multi-physics dynamics engine (2016)
  20. Žbontar, Jure; Lecun, Yann: Stereo matching by training a convolutional neural network to compare image patches (2016)

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