Pipelining Computation and Optimization Strategies for Scaling GROMACS on the Sunway Many-Core Processor

Abstract

The increasing gap between plentiful computing elements and limited memory bandwidth makes it increasingly difficult and sometimes even infeasible for HPC community to port more applications onto many-core processor architectures. The Sunway many-core processor SW26010 used to build the Sunway TaihuLight System contains a total of 260 heterogeneous cores. All these cores can be divided into 4 core groups (CGs). Each CG includes a Management Processing Element (MPE) core and 64 Computing Processing Elements (CPEs) cores. In this paper, we refactor an important molecular dynamics (MD) application GROMACS on the Sunway Taihulight system. By rewriting the compute-intensive kernel of GROMACS, we exploit a suitable parallelism for CPE cluster and implement pipelining computation between MPE and CPE cluster. Optimization strategies including the efficient use of scratchpad, the software-emulated cache and a hybrid parallel algorithm are adopted to solve the challenging memory bandwidth limitation. When comparing the refactored version using MPE and 64 CPEs with the original ported version using only MPE, we achieve a 16x speedup for the compute-intensive kernel. For simulating a molecule with 3 million atoms, we currently have managed to scale to 798,720 cores. Moreover, we analyze the adaptability of our mapping and optimization strategies for solving the memory bandwidth limitation when refactoring a real-world application on the Sunway heterogeneous many-core processor system.