An efficient GPU implementation of cyclic reduction solver for high-order compressible viscous flow simulations

Esfahanian, Vahid, Baghapour, Behzad, Torabzadeh, Mohammad and Chizari, Hossain ORCID: https://orcid.org/0000-0002-6630-1880 2014. An efficient GPU implementation of cyclic reduction solver for high-order compressible viscous flow simulations. Computers and Fluids 92 , pp. 160-171. 10.1016/j.compfluid.2013.12.011

Full text not available from this repository.

Abstract

In this paper, the performance of the Cyclic Reduction (CR) algorithm for solving tridiagonal systems is improved with the aid of efficient global memory transactions on Graphics Processing Units (GPU). To achieve maximum memory throughput with a lower computational runtime, two different Sort algorithms are introduced for reordering the initial system of equations: direct and step-by-step. It is shown that the latter method is well-fitted to modern GPUs and achieves speedup of up to 3.47× in single precision and 2.1× in double precision compared to the CPU Thomas algorithm. By benefiting from the new global memory implementation, the CR solver could run 2×–100× faster compared to previous works on parallel tridiagonal solvers. The CR solver is also applied to 2D & 3D compressible viscous flow simulations using the high-order compact finite-difference scheme. In this matter, the procedure of filtering, primitive variables, and flux derivative calculations are carried out by using the parallel tridiagonal solver on the GPU device. The GPU-accelerated calculations achieve speedups between 1.9×–15.2× in 2D and 6.4×–20.3× in 3D simulations for different grid sizes compared to CPU computations. The computations are performed on the NVIDIA GTX480 GPU. The obtained results are compared to those achieved on a single core of Intel Core 2 Duo (2.7 GHz, 2 MB cache) in terms of calculation runtime.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0045-7930
Date of Acceptance:	9 December 2013
Last Modified:	05 Nov 2022 02:42
URI:	https://orca.cardiff.ac.uk/id/eprint/135204

Citation Data

Cited 11 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item