Abstract
Randomness comes in many flavours and has countless applications in various domains which state different quality requirements for the outcome of random number generators, therefore decisions on the suitability of a randomness generator for a specific application has to be made as a result of thorough analysis including the essential part of statistical testing. But as systems tend to consume increasingly larger volumes of random data, having high throughput random number generators is imperative, but not sufficient, because between generators and the application requiring randomness the data can flow with a speed limited by the performance of the statistical testing units interposed. Furthermore statistical tests can assess only certain features of random sequences based on the statistical properties of true random sequences but can not ensure perfect randomness, hence several statistical tests have to be applied in order to increase the confidence in the selected generator. As a result there is a stringent need for high performance test suites for assessing the quality of the generated random sequences. Our work enrols in this direction presenting a performance efficient version of the well known NIST statistical test suite for random and pseudorandom number generators based on a paradigm shift towards byte stream processing mode inside the tests. Experimental results show significant performance improvements of up to 13 times in average compared to the original version.
Published Version
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