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arxiv: 1512.06025 · v2 · pith:OY46GH3Vnew · submitted 2015-12-18 · 🧮 math.NA

GPU-accelerated Bernstein-Bezier discontinuous Galerkin methods for wave problems

classification 🧮 math.NA
keywords bernstein-bezierkernelsblock-partitionedcomputationaldiscontinuousgalerkingpu-acceleratednodal
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We evaluate the computational performance of the Bernstein-Bezier basis for discontinuous Galerkin (DG) discretizations and show how to exploit properties of derivative and lift operators specific to Bernstein polynomials for an optimal complexity quadrature-free evaluation of the DG formulation. Issues of efficiency and numerical stability are discussed in the context of a model wave propagation problem. We compare the performance of Bernstein-Bezier kernels to both a straightforward and a block-partitioned implementation of nodal DG kernels in a time-explicit GPU-accelerated DG solver. Computational experiments confirm the advantage of Bernstein-Bezier DG kernels over both straightforward and block-partitioned nodal DG kernels at high orders of approximation.

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