Vadali Ramkumar V, Shi Yan, Kumar Sameer, Kale Laxmikant V, Tuckerman Mark E, Martyna Glenn J
Department of Computer Science, The Siebel Center, University of Illinois, 201 N. Goodwin Avenue, Urbana, Illinois 61801-2302, USA.
J Comput Chem. 2004 Dec;25(16):2006-22. doi: 10.1002/jcc.20113.
Many systems of great importance in material science, chemistry, solid-state physics, and biophysics require forces generated from an electronic structure calculation, as opposed to an empirically derived force law to describe their properties adequately. The use of such forces as input to Newton's equations of motion forms the basis of the ab initio molecular dynamics method, which is able to treat the dynamics of chemical bond-breaking and -forming events. However, a very large number of electronic structure calculations must be performed to compute an ab initio molecular dynamics trajectory, making the efficiency as well as the accuracy of the electronic structure representation critical issues. One efficient and accurate electronic structure method is the generalized gradient approximation to the Kohn-Sham density functional theory implemented using a plane-wave basis set and atomic pseudopotentials. The marriage of the gradient-corrected density functional approach with molecular dynamics, as pioneered by Car and Parrinello (R. Car and M. Parrinello, Phys Rev Lett 1985, 55, 2471), has been demonstrated to be capable of elucidating the atomic scale structure and dynamics underlying many complex systems at finite temperature. However, despite the relative efficiency of this approach, it has not been possible to obtain parallel scaling of the technique beyond several hundred processors on moderately sized systems using standard approaches. Consequently, the time scales that can be accessed and the degree of phase space sampling are severely limited. To take advantage of next generation computer platforms with thousands of processors such as IBM's BlueGene, a novel scalable parallelization strategy for Car-Parrinello molecular dynamics is developed using the concept of processor virtualization as embodied by the Charm++ parallel programming system. Charm++ allows the diverse elements of a Car-Parrinello molecular dynamics calculation to be interleaved with low latency such that unprecedented scaling is achieved. As a benchmark, a system of 32 water molecules, a common system size employed in the study of the aqueous solvation and chemistry of small molecules, is shown to scale on more than 1500 processors, which is impossible to achieve using standard approaches. This degree of parallel scaling is expected to open new opportunities for scientific inquiry.
在材料科学、化学、固态物理和生物物理等许多极为重要的体系中,需要通过电子结构计算来产生作用力,而不是依靠经验推导的力定律来充分描述其性质。将此类力用作牛顿运动方程的输入,构成了从头算分子动力学方法的基础,该方法能够处理化学键断裂和形成过程的动力学。然而,为了计算从头算分子动力学轨迹,必须进行大量的电子结构计算,这使得电子结构表示的效率和准确性成为关键问题。一种高效且准确的电子结构方法是采用平面波基组和原子赝势实现的对Kohn-Sham密度泛函理论的广义梯度近似。正如卡尔(R. Car)和帕里内洛(M. Parrinello)所开创的(《物理评论快报》1985年,第55卷,第2471页),梯度校正密度泛函方法与分子动力学的结合已被证明能够阐明许多复杂体系在有限温度下的原子尺度结构和动力学。然而,尽管这种方法具有相对较高的效率,但使用标准方法在中等规模系统上,当处理器数量超过几百个时,该技术无法实现并行扩展。因此,可访问的时间尺度和相空间采样程度受到严重限制。为了利用具有数千个处理器的下一代计算机平台,如IBM的蓝色基因(BlueGene),利用Charm++并行编程系统所体现的处理器虚拟化概念,开发了一种用于卡尔-帕里内洛分子动力学的新型可扩展并行化策略。Charm++允许卡尔-帕里内洛分子动力学计算的各种元素以低延迟交织,从而实现前所未有的扩展。作为一个基准,一个由32个水分子组成的系统(这是研究小分子水合作用和化学性质时常用的系统规模)在超过1500个处理器上实现了扩展,而这是使用标准方法无法实现的。这种程度的并行扩展有望为科学探索带来新的机遇。