Resolution of an inverse thermal problem using parallel processing on shared-memory multiprocessor architectures

作     者：Ansoni, J. L. Brandi, A. C. Seleghim, P., Jr. 

作者机构：Univ Sao Paulo Dept Mech Engn Engn Sch Sao Carlos Sao Carlos SP Brazil 

出 版 物：《INVERSE PROBLEMS IN SCIENCE AND ENGINEERING》 (科学与工程的逆问题)

年 卷 期：2015年第23卷第2期

页      面：351-375页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0701[理学-数学] 

基　　金：CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior) FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo) [proc. 2010/00442-5] Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [10/00442-5] Funding Source: FAPESP 

主　　题：inverse thermal problem preconditioned conjugate gradient method parallel computation multi-core processing graphics processing units 

摘      要：Advances in multi-cores CPUs and in Graphics Processors Units (GPUs) are attracting a lot of attention of the scientific community due to their parallel processing power in conjunction with their low cost. In recent years the resolution of inverse thermal problems (ITP) is gaining increasing importance and attention in simulation-based applied science and engineering. However, the resolutions of these problems are very sensitive to random errors and the computer cost is high. In an attempt to improve the computational performance to solve an ITP, the computational power of multi-core architectures was used and analysed;mainly those offered by the GPU via Compute Unified Device Architecture (CUDA) and multi-cores CPUs via Pthreads. Also, we developed the implementation of the Preconditioned Conjugate Gradient method as a kernel on GPU to solve several sparse linear systems. Our CUDA and Pthreads-based systems are, respectively, two and four times faster than the serial version, while maintaining comparable convergence behaviour.