Parallel implementation of the BiCGStab(2) method in GPU using CUDA and matlab for solution of linear systems

Abstract

This paper presents a parallel implementation of the hybrid BiCGStab(2) (bi-conjugate gradient stabilized) iterative method in a GPU (graphics processing unit) for solution of large and sparse linear systems. This implementation uses the CUDA-Matlab integration, in which the method operations are performed in a GPU core using Matlab built-in functions. The goal is to show that the exploitation of parallelism by using this new technology can provide a significant computational performance. For the validation of the work, we compared the proposed implementation with a BiCGStab(2) sequential and parallelized implementation in the C and CUDA-C languages. The results showed that the proposed implementation is more efficient and can be viable for simulations being carried out with quality and in a timely manner. The gains in computational efficiency were 76x and 6x compared to the implementation in C and CUDA-C, respectively.