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Efficient parallel algorithms in global optimization of potential energy functions for peptides, proteins, and crystals

机译:全局优化肽,蛋白质和晶体的势能函数的高效并行算法

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Global optimization is playing an increasing role in physics, chemistry, and biophysical chemistry. One of the most important applications of global optimization is to find the global minima of the potential energy of molecules or molecular assemblies, such as crystals. The solution of this problem typically requires huge computational effort. Even the fastest processor available is not fast enough to carry out this kind of computation in real time for the problems of real interest, e. g., protein and crystal structure prediction. One way to circumvent this problem is to take advantage of massively parallel computing. In this paper, we provide several examples of parallel implementations of global optimization algorithms developed in our laboratory. All of these examples follow the master/worker approach. Most of the methods are parallelized on the algorithmic (coarse-grain) level and one example of fine-grain parallelism is given, in which the function evaluation itself is computationally expensive. All parallel algorithms were initially implemented on an IBM/SP2 (distributed-memory) machine. In all cases, however, message passing is handled through the standard Message Passing Interface (MPI); consequently the algorithms can also be implemented on any distributed- or shared-memory system that runs MPI. The efficiency of these implementations is discussed.
机译:全局优化在物理,化学和生物物理化学中起着越来越重要的作用。全局优化的最重要应用之一是找到分子或分子组件(例如晶体)的势能的全局最小值。解决该问题通常需要大量的计算工作。即使是最快的处理器,也不能足够快地针对真正感兴趣的问题(例如,实时计算)实时进行这种计算。例如,蛋白质和晶体结构预测。规避此问题的一种方法是利用大规模并行计算。在本文中,我们提供了在实验室中开发的全局优化算法的并行实现的几个示例。所有这些示例都遵循主/工人方法。大多数方法在算法(粗粒度)级别上并行化,并给出了细粒度并行性的一个示例,其中函数评估本身在计算上很昂贵。所有并行算法最初都是在IBM / SP2(分布式内存)计算机上实现的。但是,在所有情况下,消息传递都是通过标准的消息传递接口(MPI)处理的;因此,算法也可以在运行MPI的任何分布式或共享内存系统上实现。讨论了这些实现的效率。

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