GPU optimized Pseudo Random Number Generator for MCNP

The Monte Carlo particle transport algorithms are ideally suited to parallel processing architectures and so are good candidates for acceleration using a Graphics Processor Unit (GPU). As the foundation of Monte Carlo N-Particle Transport Code (MCNP), Pseudo Random Number Generator (PRNG) should be provided with some specified nature such as long period, high quality and fast generation. Newer NVIDIA Fermi architecture based GPU offer a dramatic performance improvement in double precision, which provides a good fundament for an effective implementation of PRNG. This paper presents an effective implementation of the 48bit PRNG algorithm proposed in MPI version of MCNP on GPU. After the optimization of GPU memory utilization and execution parameters of our PRNG, experimental results show that the performance speedup of one NVIDIA M2050 GPU with full double precision floating operations is up to 11-fold factor compared with the parallel implementation on one multi-core Intel Xeon X5670.