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Single-Instruction, Multiple-Data (SIMD) random number generators (RNGs) takeadvantage of vector units to offer significant performance gain overnon-vectorized libraries, but they often rely on batch production of deviatesfrom distributions with fixed parameters. In many statistical applications suchas Gibbs sampling, parameters of sampled distributions change from oneiteration to the next, requiring that random deviates be generatedone-at-a-time. This situation can render vectorized RNGs inefficient, and eveninferior to their scalar counterparts. The C++ class BatchRNG uses buffers ofbase distributions such uniform, Gaussian and exponential to take advantage ofvector units while allowing for sequences of deviates to be generated withvarying parameters. These small buffers are consumed and replenished as neededduring a program execution. Performance tests using Intel Vector StatisticalLibrary (VSL) on various probability distributions illustrates theeffectiveness of the proposed batching strategy.

Efficient SIMD RNG for Varying-Parameter Streams: C++ Class BatchRNG