标题： 无需通信的中性生物多样性模型并行仿真 显示英文标题

标题： Communication-free and Parallel Simulation of Neutral Biodiversity Models

摘要： 我们提出了一种新颖的无需通信的算法，用于基于个体的概率中性生物多样性模拟。 该算法通过在进程间通信上进行权衡，以牺牲一些冗余计算为代价，将中性Moran生态系统模型转换为一个极易并行的问题。 具体而言，通过精心设计驱动模拟的随机数生成器，我们安排了进化亲代-子代交互，而无需了解交互本身、其参与者或哪个处理器正在执行计算。 关键的是，这意味着每个个体都可以完全独立地进行模拟。 因此，无论模拟分布在多少个处理器上，都可以完全重现。 通过我们新颖的算法，模拟可以(1)拆分为独立的批量任务，并且(2)在任意数量的异构机器上进行模拟——而不会影响模拟结果。 我们使用Rust编程语言构建了可扩展且静态检查的模拟包$\texttt{necsim-rust}$。 我们通过将三种传统的模拟算法与我们独立算法的CPU和GPU实现进行比较来评估我们的并行化方法。 这些实验表明，只要保持一些局部状态以消除冗余个体，我们的独立算法与现有的顺序解决方案一样高效。 GPU实现进一步以从$\sim 2$到$\sim 80$的倍数优于所有CPU上的算法，具体取决于模型参数化和所进行的分析。 在我们研究的并行算法中，我们的独立算法提供了唯一一种可以扩展到大规模模拟域的非近似并行化策略。 显示英文摘要

摘要： We present a novel communication-free algorithm for individual-based probabilistic neutral biodiversity simulations. The algorithm transforms a neutral Moran ecosystem model into an embarrassingly parallel problem by trading off inter-process communication at the cost of some redundant computation. Specifically, by careful design of the random number generator that drives the simulation, we arrange for evolutionary parent-child interactions to be modelled without requiring knowledge of the interaction, its participants, or which processor is performing the computation. Critically, this means that every individual can be simulated entirely independently. The simulation is thus fully reproducible irrespective of the number of processors it is distributed over. With our novel algorithm, a simulation can be (1) split up into independent batch jobs and (2) simulated across any number of heterogeneous machines - all without affecting the simulation result. We use the Rust programming language to build the extensible and statically checked simulation package $\texttt{necsim-rust}$. We evaluate our parallelisation approach by comparing three traditional simulation algorithms against a CPU and GPU implementation of our Independent algorithm. These experiments show that as long as some local state is maintained to cull redundant individuals, our Independent algorithm is as efficient as existing sequential solutions. The GPU implementation further outperforms all algorithms on the CPU by a factor ranging from $\sim 2$ to $\sim 80$, depending on the model parameterisation and the analysis that is performed. Amongst the parallel algorithms we have investigated, our Independent algorithm provides the only non-approximate parallelisation strategy that can scale to large simulation domains.