Performance modeling and optimization of parallel LU-SGS on many-core processors for 3D high-order CFD simulations

As a typical Gauss–Seidel method, the inherent strong data dependency of lower-upper symmetric Gauss–Seidel (LU-SGS) poses tough challenges for shared-memory parallelization. On early multi-core processors, the pipelined parallel LU-SGS approach achieves promising scalability. However, on emerging many-core processors such as Xeon Phi, experience from our in-house high-order CFD program show that the parallel efficiency drops dramatically to less than 25%. In this paper, we model and analyze the performance of the pipelined parallel LU-SGS algorithm, present a two-level pipeline (TL-Pipeline) approach using nested OpenMP to further exploit fine-grained parallelisms and mitigate the parallel performance bottlenecks. Our TL-Pipeline approach achieves 20% performance gains for a regular problem $$(256\times 256\times 256)$$(256×256×256) on Xeon Phi. We also discuss some practical problems including domain decomposition and algorithm parameters tuning for realistic CFD simulations. Generally, our work is applicable to the shared-memory parallelization of all Gauss–Seidel like methods with intrinsic strong data dependency.