MRI: Acquisition of a GPU-Enabled Computer Cluster for Molecular Modeling Applications

该奖项由主要研究仪器（MRI），化学研究仪器和刺激竞争性研究的实验计划（EPSCoR）计划支持。来自堪萨斯州立大学的克莉丝汀·艾肯斯教授和他的同事保罗·史密斯、丹尼尔·安德森、刘斌和杰弗里·科默正在购买一个图形处理单元（GPU）计算机集群。通常，计算机集群是以中央处理器（CPU）作为核心计算硬件来构建的。GPU随后被开发为处理单元，并用于加速计算机显示器上的视频显示。计算科学家发现，CPU/GPU系统的组合对于某些计算特别快，特别是模拟。为计算模拟开发的软件已经达到了可以为各种分子、生物分子、纳米颗粒和材料建模应用提供定量或半定量准确性的阶段。这些模拟提供了所需的原子级洞察化学机制和生物相互作用，很难在实验上研究。该系统满足化学、物理、工程和生物学领域分子建模应用的计算需求。该系统用于堪萨斯州立大学的研究和培训。来自全州的研究人员，包括社区大学的研究人员，非博士生。赠款机构和为少数群体服务的机构也可以使用这一系统，并获得大量的计算时间。它有助于模拟纳米材料;理解电子-电子和电子-核相互作用的基本原理。该簇也用于阐明参与确定醇脱水产物在沸石催化剂上的选择性的因素，并进行蛋白质-纳米颗粒建模以了解蛋白质构象转变，从而对相关蛋白质-表面构型进行采样。计算机集群用于理解蛋白质折叠中的变性状态集合，并理解可能与阿尔茨海默病相关的蛋白质聚集的作用。

This award is supported by the Major Research Instrumentation (MRI), the Chemistry Research Instrumentation, and the Experimental Program to Stimulate Competitive Research (EPSCoR) programs. Professor Christine Aikens from Kansas State University and colleagues Paul Smith, Daniel Andresen, Bin Liu, and Jeffrey Comer are acquiring a graphic processing unit (GPU) computer cluster. In general, computer clusters are constructed with central processing units (CPU) as the core computational hardware. GPUs were subsequently developed as processing units and used to accelerate the video display on computer monitors. Computational scientists have found a combination of CPU/GPU systems are especially fast for certain computations, especially simulations. Software developed for computational simulations have reached the stage at which they can provide quantitative or semi-quantitative accuracy for a variety of molecular, biomolecular, nanoparticle, and materials modeling applications. These simulations provide needed atomic-level insight into chemical mechanisms and biological interactions that are difficult to study experimentally. This system provides for the computational needs of molecular modeling applications across chemistry, physics, engineering and biology. This system is used for research and training at Kansas State University. Researchers from across the state including those at community colleges, non-Ph.D. granting institutions, and minority-serving institutions also have access to this system and receive significant computing time.This computer cluster enhances research and education. It aids the modeling nanomaterials; understanding the fundamentals of electron-electron and electron-nuclear interactions. This cluster is also used in elucidating the factors involved in determining the selectivities of alcohol dehydration products on zeolite catalysts and carrying out protein-nanoparticle modeling to understand protein conformational transitions that sample relevant protein-surface configurations. The computer cluster is used in understanding denatured state ensembles in protein folding and understanding the role of protein aggregation which may be relevant to Alzheimer's disease.