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

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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.