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

协作研究：要素：SciMem：在大内存平台上实现高性能多尺度仿真

• 批准号: 2104116
• 负责人: Dong Li
• 金额: $ 45.99万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104116&HistoricalAwards=false
• 关键词: Collaborative; Research; Elements; SciMem; Enabling

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.

提高系统的可扩展性对于提高国家的科学应用计算能力至关重要。然而，一些应用程序经常面临的可扩展性的挑战，从内存容量的角度来看。在处理来自不同尺度的大量模拟数据时，这在多尺度模拟中尤其如此。新兴的大内存基础设施已经显示出巨大的潜力，以增加模拟规模和解决更大的数值问题。然而，使用大内存架构的多尺度模拟是具有挑战性的，因为在大内存机器的有限的计算能力和内存异构性引入大内存。缺乏一个软件基础设施，可以释放大内存的全部功能，以加速多尺度模拟。该项目旨在创建一个功能和一个软件包（名为SciMem），使大内存平台上的高性能多尺度仿真。所提出的技术提供了一个路径，这种结构的广泛的各种应用具有广泛的影响，科学和工程的一般使用。通过学生直接参与该项目并与教育活动相结合，将对学生产生影响。该项目将通过更有效地利用大型异构内存机器，在大型内存平台上实现高性能多尺度模拟。具体地说，它将在异构计算系统上用预先计算并存储在内存中的数据代替计算。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）集成和测试。在计算资源的使用方面的发展改进将允许在新兴的硬件系统上执行复杂物理现象的更精确的模型。SciMem旨在为广泛应用于计算化学和材料科学领域的某些大规模多尺度模拟带来10倍的性能提升，例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。