Optimizing Preconditioned Conjugate Gradient on TaihuLight for OpenFOAM

Abstract

Porting the domain-specific software OpenFOAM onto the TaihuLight supercomputer is a challenging task, due to the highly memory-bound nature of both the supercomputer's processor (SW26010) and the software's liner solvers. Our study tackles this technical challenge, in three steps, by optimizing the linear solvers, such as Preconditioned Conjugate Gradient (PCG), on the SW26010. First, in order to minimize the all_reduce communication cost of PCG, we developed a new algorithm RNPCG, a non-blocking PCG leveraging the on-chip register communication. Second, we optimized three key kernels of the PCG, including proposing a localized version of the Diagonal-based Incomplete Cholesky (LDIC) preconditioner. Third, to scale the RNPCG on TaihuLight, we designed the three-level non-blocking all_reduce operations. With these three steps, we implemented the RNPCG in OpenFOAM. The experimental results on TaihuLight show that 1) compared with the default implementations of OpenFOAM, the RNPCG and the LDIC on a single-core group of SW26010 can achieve a maximum speedup of 8.9X and 3.1X, respectively; 2) the scalable RNPCG can outperform the standard PCG both in the strong and the weak scaling up to 66,560 cores.