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A parallel Monte Carlo search algorithm for the conformational analysis of proteins

机译:用于蛋白质构象分析的并行蒙特卡洛搜索算法

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The EDMC (electrostatically driven Monte Carlo) method has proven to be an effective computational tool for searching the potential energy hypersurface of polypeptide molecules consisting of up to 20 amino acid residues. Such a Monte Carlo search combined with gradient-based energy minimization of molecular conformations results in the need for 100 gigaflop or higher performance levels. The parallel EDMC algorithm has been designed to exploit currently available supercomputing technology. The implementation on the iPSC/2 described appears to represent an improvement over the original version for the IBM 3090. A performance analysis indicates that the attainable parallelism is limited by the underlying acceptance rate of search. It is demonstrated that a coarse-grained approach is suitable for architectures such as the CRAY-XMP, particularly if vectorization techniques can be exploited. Tests on the Intel iPSC/2-VX computer have shown, however, that even the easily vectorized parts of the computation may not overcome a large vector pipeline latency.
机译:EDMC(静电驱动的蒙特卡洛)方法已被证明是一种有效的计算工具,可用于搜索由多达20个氨基酸残基组成的多肽分子的势能超表面。这种蒙特卡罗搜索与分子构象的基于梯度的能量最小化相结合,导致需要100千兆位或更高的性能水平。并行EDMC算法已被设计为利用当前可用的超级计算技术。所描述的iPSC / 2上的实现似乎表示对IBM 3090原始版本的改进。性能分析表明,可达到的并行度受基本搜索接受率的限制。事实证明,粗粒度方法适用于诸如CRAY-XMP之类的体系结构,尤其是在可以利用矢量化技术的情况下。但是,在Intel iPSC / 2-VX计算机上的测试表明,即使是容易矢量化的计算部分也可能无法克服较大的矢量流水线延迟。

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