Random Number Generation Bottleneck: It's Not Your PRNG
This article uses a story to highlight a key issue in optimizing random number generation algorithms: the bottleneck may not be the PRNG itself, but the method of generating random numbers within a specific range. The author compares several methods for generating random numbers within a given range, including classic modulo, floating-point multiplication, integer multiplication, and several unbiased methods such as rejection sampling and bitmasking. Experimental results show that the best method varies depending on the PRNG and data scale, but Lemire's integer multiplication-based method, after optimization, performs exceptionally well, significantly improving performance. The article also compares the performance of various PRNGs, finding that even the fastest PRNGs offer far less performance improvement than optimizing the range generation method.
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