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 692 articles , 2 standard articles )

Showing results 1 to 20 of 692.
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  1. Afzal, Asif; Ansari, Zahid; Rimaz Faizabadi, Ahmed; Ramis, M.K.: Parallelization strategies for computational fluid dynamics software: state of the art review (2017)
  2. Alonso, Pedro; Ibáñez, Javier; Sastre, Jorge; Peinado, Jesús; Defez, Emilio: Efficient and accurate algorithms for computing matrix trigonometric functions (2017)
  3. Amaral, Sergio; Allaire, Douglas; Willcox, Karen: Optimal $L_2$-norm empirical importance weights for the change of probability measure (2017)
  4. Antti-Pekka Hynninen, Dmitry I. Lyakh: cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs (2017) arXiv
  5. Boubekeur, Mohamed; Benkhaldoun, Fayssal; Seaid, Mohammed: GPU accelerated finite volume methods for three-dimensional shallow water flows (2017)
  6. Cedric Nugteren: CLBlast: A Tuned OpenCL BLAS Library (2017) arXiv
  7. Chen, Tianran; Lee, Tsung-Lin; Li, Tien-Yien: Mixed cell computation in HOM4ps (2017)
  8. Chen, Tianran; Mehta, Dhagash: Parallel degree computation for binomial systems (2017)
  9. Conte, Dajana; Paternoster, Beatrice: Parallel methods for weakly singular Volterra integral equations on GPUs (2017)
  10. de Paula, Lauro Cássio Martins; Soares, Anderson S.; Soares, Telma W.L.; Filho, Arlindo R.G.; Coelho, Clarimar J.; Delbem, Alexandre C.B.; Martins, Wellington S.: Parallel regressions for variable selection using GPU (2017)
  11. Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
  12. Ingo Steinwart, Philipp Thomann: liquidSVM: A Fast and Versatile SVM package (2017) arXiv
  13. Lefticaru, Raluca; Macías-Ramos, Luis F.; Niculescu, Ionuţ Mihai; Mierlă, Laurenţiu: Agent-based simulation of kernel P systems with division rules using FLAME (2017)
  14. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  15. Peter Steinbach, Matthias Werner: gearshifft - The FFT Benchmark Suite for Heterogeneous Platforms (2017) arXiv
  16. Phipps, E.; D’Elia, M.; Edwards, H.C.; Hoemmen, M.; Hu, J.; Rajamanickam, S.: Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures (2017)
  17. Rafael B. Frigori: PHAST: Protein-like heteropolymer analysis by statistical thermodynamics (2017) arXiv
  18. Shavlyugin, A.I.: Development of the instability of an axisymmetric vortex flow in a circular cylinder (2017)
  19. Tingelstad, Lars; Egeland, Olav: Automatic multivector differentiation and optimization (2017)
  20. Tranquilli, Paul; Glandon, S.Ross; Sarshar, Arash; Sandu, Adrian: Analytical Jacobian-vector products for the matrix-free time integration of partial differential equations (2017)

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