BENCHMARKING AND PARALLEL ALGORITHM IMPROVEMENT FOR CHARMM WITH AN APPLICATION

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 尽管存在一些速度更快的基本分子动力学(MD)算法，但没有一种算法拥有像CHARMM那样完全实现的采样方法。CHARMM中先进的采样方法对于有意义的分子模拟至关重要，尤其是对大的生物分子。改进CHARMM MD引擎的并行实现，以充分利用现代大规模并行体系结构，将使CHARMM能够与其他顶级MD程序的性能相媲美。我们需要更多的大型超级计算机资源和架构来测试和开发该算法。我们的具体科学项目(碳水化合物建模)和更大的CHARMM用户社区将从这一发展中受益匪浅。碳水化合物模型与我们的一大笔SciDAC赠款相联系，以了解酶催化纤维素降解的生化机制，其中碳水化合物识别和糖基化起着重要作用。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Although a number of faster basic molecular dynamics (MD) algorithms exist, none have nearly as many fully implemented sampling methods as CHARMM. The advanced sampling methods in CHARMM are critical for meaningful molecular simulation, especially for large biomolecules. Improvement of the parallel implementation of the CHARMM MD engine to fully take advantage of modern massively parallel architecture will allow CHARMM to match the performance of other top MD programs. We need more large supercomputer resources and architectures to test and develop the algorithm. Our specific scientific project (carbohydrate modeling) and the larger CHARMM user community will greatly benefit from this development. Carbohydrate modeling connects to one of our large SciDAC grants to understand the biochemical mechanism in enzyme-catalyzed cellulose degradation, in which carbohydrate recognition and glycosylation play important roles.