Optimization of Finite-Differencing Kernels for Numerical Relativity Applications

Optimization of Finite-Differencing Kernels for Numerical Relativity Applications

Blog Article

A simple optimization strategy for the computation of 3D finite-differencing kernels on many-cores architectures is proposed.The 3D finite-differencing computation is split direction-by-direction and exploits curash water wipes 6x80 two level of parallelism: in-core vectorization and multi-threads shared-memory parallelization.The main application of this method is to accelerate the high-order stencil computations in numerical relativity codes.Our proposed method provides substantial speedup in computations involving tensor contractions and 3D mega motion lc100 stencil calculations on different processor microarchitectures, including Intel Knight Landing.