The influence of random number generation in dissipative particle dynamics simulations using a cryptographic hash function.

Kiyoshiro Okada,Paul E Brumby,Kenji Yasuoka,Yaroslav V Kudryavtsev

doi:10.1371/journal.pone.0250593

Abstract

The tiny encryption algorithm (TEA) is widely used when performing dissipative particle dynamics (DPD) calculations in parallel, usually on distributed memory systems. In this research, we reduced the computational cost of the TEA hash function and investigated the influence of the quality of the random numbers generated on the results of DPD calculations. It has already been established that the randomness, or quality, of the random numbers depend on the number of processes from internal functions such as SHIFT, XOR and ADD, which are commonly referred to as "rounds". Surprisingly, if we choose seed numbers from high entropy sources, with a minimum number of rounds, the quality of the random numbers generated is sufficient to successfully perform accurate DPD simulations. Although it is well known that using a minimal number of rounds is insufficient for generating high-quality random numbers, the combination of selecting good seed numbers and the robustness of DPD simulations means that we can reduce the random number generation cost without reducing the accuracy of the simulation results.

Highlights

Particle-based simulation methods are powerful tools with which to study microscopic stochastic systems
We already know that the properties of the generated random numbers are not as good as they could be, we have evaluated the properties we expect to be the minimum necessary for use in dissipative particle dynamics (DPD) simulation as follows
We tried to reduce the calculation cost of random number generation when using tiny encryption algorithm (TEA) algorithms and to check whether the properties of these random numbers will impact the results of DPD simulations

Summary

Introduction

Particle-based simulation methods are powerful tools with which to study microscopic stochastic systems. In order to make valid comparisons with experimental data, large-scale simulations are often required. This entails the simulation of large numbers of particles. This can be costly in terms of computational resources, the rapid development of faster and more efficient hardware such as many-core computers or graphic processing units (GPUs), coupled with new and improved calculation methods, permits us to perform such large-scale simulations. The use of coarse-grained (CG) methods is an effective way to perform large-scale simulations. One such method is dissipative particle dynamics (DPD) [3,4,5]. Due to the broad applicability of this method, it has seen use for the simulation of polymers, [6,7,8,9,10] biological

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Results

Conclusion