Adaptive Multi-Resolution Massively-Multicore Hybrid Dynamics

我们建议开发高度可扩展的软件，将利用下一代，异构，大规模并行处理器（如那些广泛使用的图形处理器-GPU），以提供数量级的性能提高构象采样的分子模拟。该软件将普遍适用于任何凝聚相分子系统的模拟。最初的应用领域将是加速蛋白质构象变化的采样，在制药工业中用于合理药物设计的模拟类型中，合理药物发现的未来应用将严重依赖于模拟蛋白质构象变化和蛋白质柔性的能力。以前成功应用计算方法在合理的药物设计目标蛋白质，有小的，明确的结合口袋，在蛋白质，要么是相对刚性，或改变很少药物结合。越来越多的医学感兴趣的蛋白质靶点具有大的、开放的和灵活的结合位点。为了理解结合，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。与此相结合，正在开发新一代药物，这些药物靶向蛋白质表面之间的相互作用，或者需要对蛋白质-蛋白质缔合进行建模。在这些情况下，结合位点是非常动态的，因为它是在两个（或多个）蛋白质之间形成的。现有的分子建模算法和软件无法应对建模高度灵活的蛋白质的挑战。为了保证计算科学在制药工业中继续发挥重要作用，迫切需要新的软件和新的算法。我们设计了一种新的多分辨率算法，它可以将分子动力学模拟分为两部分：近场原子部分和远场粗颗粒部分。近场部分用于模拟相邻分子之间的相互作用，使用传统的原子力场，并使用标准的蒙特卡罗（MC）算法来模拟单个原子的动力学。远场部分使用粗粒（珠状）力场模拟剩余的分子相互作用，并使用刚体动力学模拟全局动力学（例如大规模蛋白质构象变化）。这种动力学和分子相互作用建模的多分辨率分割使得该算法非常适合异构计算平台，例如配备数值加速器（例如图形处理器）的超级计算机。此外，该软件还将具有能源意识，因为执行模拟每个部分的能源成本将被考虑到分配哪些资源的决策中。例如，如果仿真的结果不是立即需要的，那么仿真可以从加速器转移，而是使用低功耗处理器（例如Intel Atoms集群，如上网本中的那些）运行。这将使模拟器选择最小化总模拟运行时间或总CO2成本。虽然是为今天的集群开发的，但该软件将很容易扩展到明天的千万亿次和亿万次超级计算机，其中软件适应性，能源管理和容错等概念将是实现高效扩展和高效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促进国际HPC社区对能源感知算法和CO2/能源感知调度的更好理解。我们的目的是正面解决国际HPC社区面临的能源成本和可用性不断增加的问题，以及显着提高能源效率和降低HPC环境成本的需求。

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.