Selecting the Best Tridiagonal System Solver Projected on Multi-Core CPU and GPU Platforms
Nowadays multicore processors and graphics cards are commodity hardware that can be found in personal computers. Both CPU and GPU are capable of performing high-end computations. In this paper we present and compare parallel implementations of two tridiagonal system solvers. We analyze the cyclic reduction method, as an example of fine-grained parallelism, and Bondelis algorithm, as a coarse-grained example of parallelism. Both algorithms are implemented for GPU architectures using CUDA and multi-core CPU with shared memory architectures using OpenMP. The results are compared in terms of execution time, speedup, and GFLOPS. For a large system of equations, 2 22, the best results were obtained for Bondelis algorithm (speedup 1.55x and 0.84 GFLOPS) for multi-core CPU platforms while the cyclic reduction (speedup 17.06x and 5.09 GFLOPS) was the best for the case of GPU platforms.
keywords: Tridiagonal system solver, multi-core CPU, OpenMP, GPU, CUDA