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.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- 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)
- 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)
- Lutsyshyn, Y.: Fast quantum Monte Carlo on a GPU (2015)
- Demchik, Vadim: Pseudorandom numbers generation for Monte Carlo simulations on GPUs: OpenCL approach (2014)
- Norkin, B.V.: Systems simulation analysis and optimization of insurance business (2014)
- Rogers, Jonathan: Monte Carlo simulation of dynamic systems on GPU’s (2014)
- Ferreiro, A.M.; García, J.A.; López-Salas, J.G.; Vázquez, C.: An efficient implementation of parallel simulated annealing algorithm in GPUs (2013)
- Saito, Mutsuo; Matsumoto, Makoto: Variants of Mersenne Twister suitable for graphic processors (2013)
- Cardoso, Nuno; Bicudo, Pedro: SU(2) lattice gauge theory simulations on Fermi GPUs (2011)