Although parallelization of computationally intensive algorithms has become a standard with the scientific community, the possibility of in-core vectorization is often overlooked. With the development of modern HPC architectures, however, neglecting such programming techniques may lead to inefficient code hardly utilizing the theoretical performance of nowadays CPUs. The presented paper reports on explicit vectorization for quadratures stemming from the Galerkin formulation of boundary integral equations in 3D. To deal with the singular integral kernels, two common approaches including the semi-analytic and fully numerical schemes are used. We exploit modern SIMD (Single Instruction Multiple Data) instruction sets to speed up the assembly of system matrices based on both of these regularization techniques. The efficiency of the code is further increased by standard shared-memory parallelization techniques and is demonstrated on a set of numerical experiments. 
Acceleration of boundary element method by explicit vectorization