The impact of pseudorandom number quality on P-RnaPredict , a parallel genetic algorithm for RNA secondary structure prediction

Abstract

This paper presents a parallel version of RnaPredict, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA's performance. The three generators tested are the C standard library PRNG RAND, a parallelized multiplicative congruential generator (MCG), and a parallelized Mersenne Twister (MT). A fully parallel version of RnaPredict using the Message Passing Interface (MPI) was implemented. The PRNG comparison tests were performed with known structures that are 118, 122, 543, and 556 nucleotides in length. The effects of the PRNGs are investigated and the predicted structures are compared to known structures.

Parametrical Tuning of Twisting Generators

“…A Pseudorandom Number Generators (PRNG) are well-known techniques with broad applications in such areas as cryptography (Tusnoo et al, 2003;Ozturk et al, 2004;Panneton et al, 2006), simulation of stochastic processes (Entacher, 1998), comprehensive testing of technical systems (Leeb and Wegenkittl, 1997;Park and Miller, 1998), medical (Menyaev and Zharov, 2006a;2006b;Menyaev and Zharova, 2006;2013;2016;Sarimollaoglu et al, 2014;Cai et al, 2016a;2016b) and biological research (Wiese et al, 2005;Leonard and Jackson, 2015;Juratly et al, 2015;2016) and others (Rababbah 2004;2007;Politano et al, 2014;2016;Riguzzi, 2016). In these publications, the concept of uniform random numbers in PRNG actively uses the operations of bit logic.…”