A GPU Accelerated BiConjugate Gradient Stabilized Solver for Speeding-up Large Scale Model Evaluation

Authors

  • Alexandru Voicu Bucharest Academy of Economic Studies, Faculty of Management

Keywords:

Non-stationary iterative method, Economics modelling, GPU, C AMP

Abstract

Solving linear systems remains a key activity in of economics modelling, therefore making fast and accurate methods for computing solutions highly desirable. In this paper, a proof of concept C++ AMP implementation of an iterative method for solving linear systems, BiConjugate Gradient Stabilized (henceforth BiCGSTAB), is presented. The method relies on matrix and vector operations, which can benefit from parallel implementations. The work contained herein details the process of arriving at a moderately parallel implementation and a widely parallel implementation. Furthermore, the construction of two typical sparse data containers in a C++ AMP friendly manner is fleshed out. The implementation is evaluated by solving a number of large-scale linear systems to an exact or ε-exact solution.

Author Biography

Alexandru Voicu, Bucharest Academy of Economic Studies, Faculty of Management

Faculty of Management, Ph.D. Student

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Published

2013-07-01

How to Cite

Voicu, A. (2013). A GPU Accelerated BiConjugate Gradient Stabilized Solver for Speeding-up Large Scale Model Evaluation. International Journal of Economic Practices and Theories, 3(3), 186-191. Retrieved from http://ijept.eu/index.php/ijept/article/view/A_GPU_Accelerated_BiConjugate_Gradient_Stabilized_Solver_for_S

Issue

Vol. 3 No. 3 (2013)

Section

Articles

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