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科学应用的计算能力至关重要。然而，从内存容量的角度来看，一些应用程序经常面临可扩展性的挑战，在处理不同规模的大量模拟数据时尤其如此。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表现出了巨大的潜力，然而，由于大内存机器的计算能力有限以及大内存引入的内存异构性，使用大内存架构进行多尺度模拟具有挑战性。目前缺乏能够释放大内存全部能力来加速多尺度仿真的软件基础设施，该项目旨在创建一种能够在大内存上进行高性能多尺度仿真的功能和软件包（名为 SciMem）。所提出的技术为这种结构的广泛应用提供了一条途径，对科学和工程产生广泛的影响。学生将通过直接参与项目并与教育相结合而产生影响。活动。该项目将实现大型的高性能多尺度模拟具体来说，它将通过在异构计算系统上预先计算和存储数据来取代计算，所开发的工具 SciMem 将与流行的并行分子进行集成和测试。动力学模拟器，LAMMPS（大规模原子/分子大规模并行模拟器）。计算资源使用方面的改进将使复杂物理现象的模型能够在新兴的硬件系统上进行。 SciMem 旨在为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 倍的性能提升，例如基于量子力学/分子力学的分子动力学（MD）催化模拟。该奖项反映了通过使用基金会的智力价值和更广泛的影响审查标准进行评估，NSF 的法定使命被认为值得支持。