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.

提高系统可伸缩性对于提高国家对科学应用的计算功能至关重要。但是，某些应用程序通常从内存能力的角度面临可伸缩性挑战。当从不同尺度处理大量模拟数据时，在多尺度模拟中尤其如此。新兴的大型存储基础设施显示出了增加模拟量表并解决更大数值问题的巨大潜力。但是，由于大型内存机器中的计算能力有限，并且由大型内存引入了大型计算能力，因此使用大型内存体系结构进行多尺度模拟受到挑战。缺乏软件基础架构，可以释放大记忆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软件包（名为SCIMEM），该软件包可以在大型内存平台上进行高性能多尺度模拟。提出的技术为这种结构的一般使用提供了一条途径，用于对科学和工程产生广泛影响的广泛应用。通过直接参与项目以及与教育活动的集成，将对学生产生影响。该项目将通过更有效地利用大型和异质的存储器来实现高性能多规模的模拟。开发的工具SCIMEM将与流行的平行分子动力学模拟器LAMMP（大规模原子/分子大量平行模拟器）进行集成和测试。开发的计算资源使用改进将允许在新兴硬件系统上进行更准确的复杂物理现象模型。 SCIMEM旨在为某些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倍性能的改进，例如，基于量子的机械/分子机械分子动力学（MD）模拟催化剂的模拟，这是NSF的法定任务和审查的范围，这是通过评估的依据来评估的。