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 597 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. Anzt, Hartwig; Chow, Edmond; Saak, Jens; Dongarra, Jack: Updating incomplete factorization preconditioners for model order reduction (2016)
  4. Bernaschi, Massimo; Bisson, Mauro; Fantozzi, Carlo; Janna, Carlo: A factored sparse approximate inverse preconditioned conjugate gradient solver on graphics processing units (2016)
  5. Bialas, Piotr; Strzelecki, Adam: Benchmarking the cost of thread divergence in CUDA (2016)
  6. Bock, Nicolas; Challacombe, Matt; Kalé, Laxmikant V.: Solvers for $\mathcalO(N)$ electronic structure in the strong scaling limit (2016)
  7. Boschetti, Marco A.; Maniezzo, Vittorio; Strappaveccia, Francesco: Using GPU computing for solving the two-dimensional guillotine cutting problem (2016)
  8. Brzeziński, Dariusz W.; Ostalczyk, Piotr: Numerical calculations accuracy comparison of the inverse Laplace transform algorithms for solutions of fractional order differential equations (2016)
  9. Chen, Yuxin; Keyes, David; Law, Kody J.H.; Ltaief, Hatem: Accelerated dimension-independent adaptive metropolis (2016)
  10. Conte, Dajana; D’Ambrosio, Raffaele; Paternoster, Beatrice: GPU-acceleration of waveform relaxation methods for large differential systems (2016)
  11. Cooper, Christopher D.; Barba, Lorena A.: Poisson-Boltzmann model for protein-surface electrostatic interactions and grid-convergence study using the PyGBe code (2016)
  12. Dai, Wei; Sunar, Berk: cuHE: a homomorphic encryption accelerator library (2016)
  13. D’Ambra, Pasqua; Filippone, Salvatore: A parallel generalized relaxation method for high-performance image segmentation on GPUs (2016)
  14. Giles, Michael B.: Algorithm 955: approximation of the inverse Poisson cumulative distribution function (2016)
  15. Joldes, Mioara; Muller, Jean-Michel; Popescu, Valentina; Tucker, Warwick: CAMPARY: cuda multiple precision arithmetic library and applications (2016)
  16. Kramer, Stephan C.; Hagemann, Johannes; Künneke, Lutz; Lebert, Jan: Parallel statistical multiresolution estimation for image reconstruction (2016)
  17. Kutyniok, Gitta; Lim, Wang-Q; Reisenhofer, Rafael: ShearLab 3D: faithful digital shearlet transforms based on compactly supported shearlets (2016)
  18. Mantas, José Miguel; de la Asunción, Marc; Castro, Manuel J.: An introduction to GPU computing for numerical simulation (2016)
  19. Matloff, Norman: Parallel computing for data science. With examples in R, C++ and CUDA (2016)
  20. Ma, Yan; Chen, Lajiao; Liu, Peng; Lu, Ke: Parallel programing templates for remote sensing image processing on GPU architectures: design and implementation (2016)

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