Collaborative Research: Elements: SciMem: Enabling High Performance Multi-Scale Simulation on Big Memory Platforms

提高系统可伸缩性是提高国家科学应用计算能力的关键。然而，从内存容量的角度来看，一些应用程序经常面临可伸缩性的挑战。当处理来自不同尺度的海量模拟数据时，在多尺度模拟中尤其如此。新兴的大内存基础设施在增加模拟规模和解决更大的数值问题方面显示出巨大的潜力。然而，由于大内存机器的计算能力有限，以及大内存带来的内存异构性，使用大内存体系结构进行多尺度仿真是具有挑战性的。缺乏能够释放大容量内存的全部能力来加速多尺度模拟的软件基础设施。这个项目的目标是创建一个功能和一个软件包(名为SciMem)，使高性能的多尺度模拟在大内存平台上实现。介绍的技术为这种结构的广泛应用提供了一条途径，对科学和工程产生了广泛的影响。该项目将通过学生直接参与该项目并通过与教育活动的整合来影响学生。该项目将通过更有效地利用大型和不同类型的存储机器，在大存储平台上实现高性能的多尺度模拟。具体地说，它将用预先计算的计算取代计算，并将其存储在异构计算系统的内存数据中。开发的工具SciMem将与流行的并行分子动力学模拟器LAMMPS(大规模原子/分子大规模并行模拟器)集成并进行测试。在计算资源使用方面的发展改进将使复杂物理现象的更准确模型能够在新兴的硬件系统上进行。本奖项旨在为在计算化学和材料科学领域广泛应用的某些更大规模的多尺度模拟带来10倍的性能改进，例如催化剂的量子力学/基于分子力学的分子动力学(MD)模拟。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Increasing system scalability is crucial to improving nation’s computation capabilities for scientific applications. However, some applications often face the scalability challenge from the perspective of memory capacity. This is especially true in multi-scale simulations when handling massive simulation data from different scales. The emerging big memory infrastructures have shown great potential to increase the simulation scale and solve larger numerical problems. However, using big memory architectures for the multi-scale simulation is challenging, because of limited computing capability in the big memory machines and memory heterogeneity introduced by big memory. There is a lack of a software infrastructure that can release the full power of big memory to accelerate multi-scale simulation. This project aims to create a capability and a software package (named SciMem) that enables high performance multi-scale simulation on big memory platforms. The techniques presented offer a path for general use of this structure for a wide variety of applications having a broad impact on science and engineering. There will be impact on the students through their direct involvement with the project and through the integration with the educational activities.The project will enable high performance multi-scale simulations on big memory platforms through more efficient utilization of large and heterogeneous memory machines. Specifically, it will replace computations with pre-computed and stored in memory data on a heterogeneous computing systems. The developed tool, SciMem, will be integrated and tested with the popular parallel molecular dynamics simulator, LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator). The developed improvements in the use of computational resources will allow more accurate models of complex physical phenomena to be carried out on the emerging hardware systems. SciMem aims to bring a 10x performance improvement for certain larger-scale multi-scale simulations widely applied in the fields of computational chemistry and material science, e.g., quantum mechanical/molecular mechanical-based molecular dynamics (MD) simulation of catalysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.