The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved with computing power along the three principal dimensions of accuracy, atom count (spatial scale), and duration (temporal scale). Since the mid-2000s, computer platforms have however failed to provide strong scaling for MD as scale-out CPU and GPU platforms that provide substantial increases to spatial scale do not lead to proportional increases in temporal scale. Important scientific problems therefore remained inaccessible to direct simulation, prompting the development of increasingly sophisticated algorithms that present significant complexity, accuracy, and efficiency challenges. While bespoke MD-only hardware solutions have provided a path to longer timescales for specific physical systems, their impact on the broader community has been mitigated by their limited adaptability to new methods and potentials. In this work, we show that a novel computing architecture, the Cerebras Wafer Scale Engine, completely alters the scaling path by delivering unprecedentedly high simulation rates up to 1.144M steps/second for 200,000 atoms whose interactions are described by an Embedded Atom Method potential. This enables direct simulations of the evolution of materials using general-purpose programmable hardware over millisecond timescales, dramatically increasing the space of direct MD simulations that can be carried out.

翻译：分子动力学（MD）模拟的发展与计算硬件的演进密切相关。自MD创建以来的数十年间，模拟性能随着计算能力在精度、原子数量（空间尺度）和模拟时长（时间尺度）这三个主要维度上同步提升。然而，自2000年代中期以来，计算平台未能为MD提供强扩展性——提供空间尺度大幅扩展的横向扩展CPU和GPU平台，并未带来时间尺度的成比例增长。因此，许多重要的科学问题仍无法通过直接模拟进行研究，这促使了日益复杂算法的开发，但这些算法在复杂性、精度和效率方面带来了重大挑战。虽然定制的专用MD硬件解决方案为特定物理系统提供了通向更长模拟时间尺度的路径，但其对新方法和势函数的有限适应性限制了它们在更广泛科研社区中的影响力。本工作中，我们展示了一种新颖的计算架构——Cerebras晶圆级引擎，通过为描述20万个原子（采用嵌入原子方法势函数描述其相互作用）的系统提供高达每秒114.4万步的模拟速率，彻底改变了扩展路径。这使得在通用可编程硬件上进行毫秒时间尺度的材料演化直接模拟成为可能，显著拓展了可直接执行的MD模拟范围。