Random Number Generation for High Performance Computing

Highly scalable parallel random number generators (RNGs) will be developed, evaluated and implemented for use in high performance computing on thousands of multi-core processors and general purpose graphics processing units. The main contributions are: (a) design and implementation of new parallel test methods that capture the inter-stream correlations exhibited in practice and complement the currently widely used sequential test batteries, (b) development of new parallel RNGs that produce 100s of thousands of high quality individual random number streams and explicitly minimize inter-stream correlations, and (c) preliminary implementation of the new test methods and parallel RNGs. The proposed RNGs will also be evaluated and tuned to generate cryptographically-secure random number streams that resist cryptanalysis attacks by insiders and eavesdroppers when used in large-scale peer-to-peer and distributed security applications. The investigators have extensive experience in the applications of random number generators, the test methods for random number generators, and the implementation of Monte Carlo applications on large clusters of processors and graphics processing units. The proposed approach balances the theoretical research with the implementation efficiencies and the use in real applications.