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

机译:使用加密散列函数的耗散粒子动力学模拟随机数产生的影响

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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.
机译:在平行执行耗散粒子动态(DPD)计算时,通常使用微小加密算法(DEA),通常在分布式存储系统上进行耗散粒子动态(DPD)计算。在这项研究中,我们降低了茶哈希函数的计算成本,并研究了在DPD计算结果上产生的随机数的质量的影响。已经确定了随机数的随机性或质量,取决于来自内部函数的过程数量,例如Shift,XOR和Add,通常称为“圆形”。令人惊讶的是,如果我们选择从高熵源的种子编号,具有最小圆数,所产生的随机数的质量就足以成功执行准确的DPD模拟。尽管众所周知,使用最小数量的圆数不足以产生高质量的随机数,所以选择良好的种子数量和DPD模拟的鲁棒性意味着我们可以降低随机数生成成本而不降低准确性仿真结果。

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