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In this paper, we present single- and multi-node optimizations of SU2, a widely-used, open-source Computational Fluid Dynamics application, aimed at improving performance and scalability for implicit Reynolds-averaged Navier–Stokes calculations on unstructured grids. Typical industry-standard implementations are currently limited by unstructured accesses, variable degrees of parallelism, as well as the global synchronizations inherent in traditionally used Krylov linear solvers. Therefore, we rely on aggressive single-node optimizations, such as hierarchical parallelism, dynamic threading, compacted memory layout, and vectorization, along with a communication-friendly agglomeration (geometric) linear multigrid solver. Based on results with the well-known ONERA M6 geometry, our single core and shared memory optimizations result in a speedup of 2.6X on the latest 14-core Intel® Xeon™ 1 E5-2697v3 processor when compared to the baseline SU2 implementation with 14 MPI ranks. In multi-node settings, the hybrid OpenMP+MPI multigrid implementation achieves 2X higher parallel efficiency on 256 nodes over conventional Krylov-based (GMRES) methods.

In recent years the computation/memory balance of processors has been continuously shifting towards computation. The rise of Deep Learning, which is based on matrix multiplications, accelerated this path, especially in terms of single precision and lower precision computation. An important research question is if this development can be leveraged for traditional HPC. In this work we demonstrate that a high order discontinuous Galerkin solver for seismic wave propagation can execute in single precision without loss of modeling accuracy. Additionally, we extended its kernels to support the Intel Knights Mill CPU with 14 TFLOPS of tensor-optimized single precision performance. This allows us to exploit the hardware's special computation capabilities, even in a regular HPC application with sparse linear algebra kernels. At the cluster-level, Knights Mill can obtain the same application performance as the latest top-bin dual socket Intel Xeon Platinum nodes. Compared to the HPC-focused Knights Landing processor, speed-ups of up to 1.6 × are possible, depending on the configuration of the solver. Additionally, we are able to increase the throughput of a quadrature-free discontinuous Galerkin method for seismic simulations by 4.2 × , when comparing our solver's single precision and fifth order performance to the SC 2017 best-paper award winning work.