BENCHMARKING AND PARALLEL ALGORITHM IMPROVEMENT FOR CHARMM WITH AN APPLICATION

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 虽然存在一些更快的基本分子动力学（MD）算法，但没有一个像CHARMM那样具有几乎一样多的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，特别是对于大的生物分子。CHARMM MD引擎的并行实现的改进，以充分利用现代大规模并行架构将允许CHARMM匹配其他顶级MD程序的性能。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算法。我们的特定科学项目（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。碳水化合物建模与我们的一个大型SciDAC赠款有关，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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.