A new class of scalable parallel pseudorandom number generators based on Pohlig-Hellman exponentiation ciphers
Paul D. Beale

TL;DR
This paper introduces a scalable, parallel pseudorandom number generator based on Pohlig-Hellman exponentiation ciphers, suitable for large-scale Monte Carlo simulations, with strong statistical properties and easy parallelization.
Contribution
It presents a novel class of pseudorandom generators using exponentiation ciphers, offering scalability, simplicity, and high-quality randomness for parallel computing environments.
Findings
Generator passes extensive correlation tests with up to 10^13 numbers.
Millions of instances with periods > 10^18 for 32-bit implementation.
128-bit version can have periods > 10^37 with over 10^15 instances.
Abstract
Parallel supercomputer-based Monte Carlo applications depend on pseudorandom number generators that produce independent pseudorandom streams across many separate processes. We propose a new scalable class of parallel pseudorandom number generators based on Pohlig--Hellman exponentiation ciphers. The method generates uniformly distributed floating point pseudorandom streams by encrypting simple sequences of integer \textit{messages} into \textit{ciphertexts} by exponentiation modulo prime numbers. The advantages of the method are: the method is trivially parallelizable by parameterization with each pseudorandom number generator derived from an independent prime modulus, the method is fully scalable on massively parallel computing clusters due to the large number of primes available for each implementation, the seeding and initialization of the independent streams is simple, the method…
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Taxonomy
TopicsChaos-based Image/Signal Encryption · Numerical Methods and Algorithms · Parallel Computing and Optimization Techniques
