Pseudo-random number generators (PRNG) have been intensively used in many stochastic algorithms in artificial intelligence, computer graphics and other scientific computing. However, the current commodity GPU design does not facilitate the efficient implementation of high-quality PRNGs that require high-precision integer arithmetics and bitwise operations. In this paper, we propose a framework to generate a high-quality PRNG shader for all kinds of GPUs. We adopt the cellular automata (CA) PRNG to facilitate high speed and parallel random number generation. The configuration of the CA PRNG is completed automatically by optimizing an objective function that accounts for quality of generated random sequences. To visually evaluate the result, we apply the best PRNG shader to photon mapping. Timing statistics show that our GPU parallelized PRNG is much faster than a pure CPU implementation.

Idea

For a Path Tracing application I need to generate good quality pseudo random numbers in the closed range [0~1]. Because I'm doing this on the GPU (HLSL Shader Model 5) I can only use 32bit