Abstract—A parallel algorithm called P-scheme/G is proposed for solving recurrence equations on GPGPU systems. This is based on P-scheme algorithm that has been originally developed for distributed memory multicomputers. In order to achieve a high performance computation on GPGPU systems, our method alleviates branch divergences by reducing the stride data accesses. We also illustrate the effectiveness of the optimal thread configuration for the recurrence equation. Our experiments with GTX 590 show that the implementation of the rearrangement using the shared memory improves the performance by 200\% to 300\% and the validity of the policy of the thread configuration is confirmed for both the constant and the non-constant parameter cases. We achieve the speedup of around 400 as a recurrence equation solver with non-constant parameters.
Index Terms—CUDA, GPU, multithreading, tridiagonal matrix solver.
Akiyoshi Wakatani is with Faculty of Intelligence and Informatics, Konan University, Kobe, Japan (e-mail: wakatani@konan-u.ac.jp).
[PDF]
Cite:Akiyoshi Wakatani, "Evaluation of P-Scheme/G Algorithm for Solving Recurrence Equations," International Journal of Modeling and Optimization vol. 3, no. 4, pp. 311-315, 2013.
