Adaptive Multi-Resolution Massively-Multicore Hybrid Dynamics

自适应多分辨率大规模多核混合动力学

基本信息

批准号：
EP/I030395/1
负责人：
Adrian Mulholland
金额：
$ 50.74万
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2011
资助国家：
英国
起止时间：
2011 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FI030395%2F1
关键词：
Adaptive Multi Resolution Massively Multicore

项目摘要

We propose to develop highly scalable software that will exploit next generation, heterogeneous, massively parallel processors (such as those found in widely available graphics processors - GPUs) to deliver orders-of-magnitude performance increases for conformational sampling in molecular simulations. The software will be generally applicable to simulations of any condensed phase molecular system. The initial application area will be to accelerate the sampling of protein conformational change within the types of simulation used for rational drug design in the pharmaceutical industry.Future applications of rational drug discovery will depend critically on the ability to model protein conformational change and protein flexibility. Previous successful applications of computational methods in rational drug design targeted proteins that had small, well-defined binding pockets, in proteins that were either relatively rigid, or changed little upon drug binding. Increasingly, medicinally interesting protein targets have large, open and flexible binding sites. To understand binding, computational models have to be able to predict how these sites will change shape upon drug binding. Coupled to this, a new generation of drugs are being developed that target the interactions between protein surfaces, or that require modelling of protein-protein association. In these cases, the binding site is extremely dynamic, as it is formed between two (or more) proteins that have come together. Existing molecular modelling algorithms and software are incapable of stepping up to the challenge of modelling highly flexible proteins. New software and new algorithms are needed urgently to ensure that computational science continues to play an important role in the pharmaceutical industry.We have designed a new multi-resolution algorithm that will allow for the simulation of molecular dynamics to be broken into two parts; a near-field, atomistic part, and a far-field, coarse grain part. The near-field part is used to model the interactions between neighbouring molecules, using traditional atomistic forcefields, and uses a standard Monte Carlo (MC) algorithm to model the dynamics of individual atoms. The far-field part models the remaining molecular interactions using a coarse-grain (beaded) forcefield, and uses rigid-body dynamics to model global dynamics (e.g large-scale protein conformational change). This multi-resolution split of both the dynamics, and the modelling of the molecular interactions, makes the algorithm ideally suited to heterogeneous computing platforms such as supercomputers equipped with numerical accelerators (e.g. graphics processors). In addition, the software will also be energy-aware, as the energy cost of performing each part of the simulation will be factored into the decision as to which resource it is allocated. For example, if the results of the simulation were not needed immediately, then the simulation could be diverted from the accelerator, and instead run using low-power processors (e.g. clusters of Intel Atoms, like those found in netbooks). This would give the simulator the choice of minimising the total simulation runtime or the total CO2 cost. While developed for the clusters of today, the software will readily scale to the peta- and exascale supercomputers of tomorrow, where concepts such as software adaptability, energy management and fault-tolerance will be key to achieving efficient scaling and efficient supercomputer utilisation. We hope that one of the lasting impacts of this project will be a promotion of greater understanding of energy-aware algorithms and CO2/energy-aware scheduling in the international HPC community. Our intention is to tackle head-on the issues facing the international HPC community in increasing yet variable energy cost and availability, and the need to significantly improve the energy efficiency, and reduce the environmental cost of HPC.

我们建议开发高度可扩展的软件，这些软件将利用下一代，异质，大规模平行的处理器（例如在广泛可用的图形处理器中发现的处理器），以提供分子模拟中构象取样的质量效果提高。该软件通常适用于模拟任何冷凝相分子系统。最初的应用领域将是加速制药行业中用于合理药物设计的模拟类型中蛋白质构象变化的采样。理性药物发现的未来应用将严格取决于建模蛋白质构象变化和蛋白质灵活性的能力。计算方法在理性药物设计中的先前成功应用靶向蛋白质，这些蛋白质具有较小的，定义明确的结合口袋，在相对刚性的蛋白质中，或者在药物结合后几乎没有改变。越来越多的药物有趣的蛋白质靶标具有较大，开放和柔性的结合位点。为了了解结合，计算模型必须能够预测这些位点在药物结合后将如何改变形状。与此同时，正在开发新一代的药物，以靶向蛋白质表面之间的相互作用，或者需要建模蛋白质蛋白质缔合。在这些情况下，结合位点非常动态，因为它是在两种（或更多）蛋白之间形成的。现有的分子建模算法和软件无法提高建模高度柔性蛋白的挑战。紧急需要新的软件和新算法，以确保计算科学在制药行业继续发挥重要作用。我们设计了一种新的多分辨率算法，该算法将允许模拟分子动力学的模拟分为两个部分。一个近场，原子片，一个远场，粗粒部分。近场部分用于使用传统的原子力场对相邻分子之间的相互作用进行建模，并使用标准的蒙特卡洛（MC）算法来对单个原子的动力学进行建模。远场部分使用粗粒（串珠）力场对剩余的分子相互作用进行建模，并使用刚体动力学来对全局动力学进行建模（例如，大规模蛋白质构象变化）。这种动力学的多分辨率拆分以及分子相互作用的建模使该算法理想地适用于配备了数值加速器（例如图形处理器）的异质计算平台（例如超级计算机）。此外，该软件也将具有能源感知，因为执行模拟每个部分的能源成本将纳入分配哪种资源的决定中。例如，如果不需要立即需要模拟的结果，则可以将仿真从加速器转移，而是使用低功率处理器（例如Intel Atoms的簇，如在上网本中发现的）运行。这将使模拟器可以选择最小化总模拟运行时或总CO2成本。尽管该软件为当今的群集开发，但该软件将很容易扩展到明天的PETA和Exascale超级计算机，在该概念中，软件适应性，能量管理和容忍度等概念将是实现有效缩放和有效的超级计算机利用的关键。我们希望该项目的持久影响之一将是对国际HPC社区中对能源感知算法和二氧化碳/能源感知的日程安排的进一步了解。我们的目的是正面解决国际HPC社区面临的问题，以增加可变的能源成本和可用性，并需要显着提高能源效率，并降低HPC的环境成本。