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In this paper, we present a novel parallel dimension-independent nodepositioning algorithm that is capable of generating nodes with variabledensity, suitable for meshless numerical analysis. A very efficient sequentialalgorithm based on Poisson disc sampling is parallelized for use onshared-memory computers, such as modern workstations with multi-coreprocessors. The parallel algorithm uses a global spatial indexing method withits data divided into two levels, which allows for an efficient multi-threadedimplementation. The addition of bootstrapping enables the algorithm to use anynumber of parallel threads while remaining as general as its sequentialvariant. We demonstrate the algorithm performance on six complex 2- and3-dimensional domains, which are either of non-rectangular shape or havevarying nodal spacing or both. We perform a run-time analysis of the algorithm,to demonstrate its ability to reach high speedups regardless of the domain andto show how well it scales on the experimental hardware with 16 processorcores. We also analyse the algorithm in terms of the effects of domain shape,quality of point placement, and various parallelization overheads.

Parallel domain discretization algorithm for RBF-FD and other meshless numerical methods for solving PDEs