The NVIDIA CUDA Random Number Generation library (cuRAND) delivers high performance GPU-accelerated random number generation (RNG). The cuRAND library delivers high quality random numbers 8x faster using hundreds of processor cores available in NVIDIA GPUs. cuRAND also provides two flexible interfaces, allowing you to generate random numbers in bulk from host code running on the CPU or from within your CUDA functions/kernels running on the GPU. A variety of RNG algorithms and distribution options means you can select the best solution for your needs.

References in zbMATH (referenced in 19 articles )

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  1. Boukaram, Wajih; Turkiyyah, George; Keyes, David: Randomized GPU algorithms for the construction of hierarchical matrices from matrix-vector operations (2019)
  2. David Diaz-Guerra, Antonio Miguel, Jose R. Beltran: gpuRIR: A python library for Room Impulse Response simulation with GPU acceleration (2018) arXiv
  3. Yianni, Panayioti C.; Neves, Luis C.; Rama, Dovile; Andrews, John D.: Accelerating Petri-net simulations using NVIDIA graphics processing units (2018)
  4. Beebe, Nelson H. F.: The mathematical-function computation handbook. Programming using the MathCW portable software library (2017)
  5. Piccinini, Enrico; Benedetti, Claudia; Siloi, Ilaria; Paris, Matteo G. A.; Bordone, Paolo: GPU-accelerated algorithms for many-particle continuous-time quantum walks (2017)
  6. Gobet, E.; López-Salas, J. G.; Turkedjiev, P.; Vázquez, C.: Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs (2016)
  7. Carrasco-Alvarez, R.; Vázquez Castillo, J.; Castillo Atoche, A.; Ortegón Aguilar, J.: A fading channel simulator implementation based on GPU computing techniques (2015)
  8. D’Amore, L.; Laccetti, G.; Romano, D.; Scotti, G.; Murli, A.: Towards a parallel component in a GPU-CUDA environment: a case study with the L-BFGS Harwell routine (2015)
  9. Lutsyshyn, Y.: Fast quantum Monte Carlo on a GPU (2015)
  10. Xu, Linlin; Ökten, Giray: High-performance financial simulation using randomized quasi-Monte Carlo methods (2015)
  11. Demchik, Vadim: Pseudorandom numbers generation for Monte Carlo simulations on GPUs: OpenCL approach (2014)
  12. Goldsworthy, M. J.: A GPU-CUDA based direct simulation Monte Carlo algorithm for real gas flows (2014)
  13. Norkin, B. V.: Systems simulation analysis and optimization of insurance business (2014)
  14. Rogers, Jonathan: Monte Carlo simulation of dynamic systems on GPU’s (2014)
  15. Sheng, Yanyan; Welling, William S.; Zhu, Michelle M.: A GPU-based Gibbs sampler for a unidimensional IRT model (2014)
  16. Ferreiro, A. M.; García, J. A.; López-Salas, J. G.; Vázquez, C.: An efficient implementation of parallel simulated annealing algorithm in GPUs (2013)
  17. Leyva, J. Francisco; Málaga, Carlos; Plaza, Ramón G.: The effects of nutrient chemotaxis on bacterial aggregation patterns with non-linear degenerate cross diffusion (2013)
  18. Saito, Mutsuo; Matsumoto, Makoto: Variants of Mersenne Twister suitable for graphic processors (2013)
  19. Cardoso, Nuno; Bicudo, Pedro: SU(2) lattice gauge theory simulations on Fermi GPUs (2011)