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.
Ao meu pai Laerte (in memoriam) pelo exemplo de vida. À minha mãe Lourdes, pela compreensão de minha ausência em muitos momentos, pelo apoio incondicional e por nunca medir esforços para prover aos filhos recursos necessários para que conseguissem estudar. Às minhas queridas irmãs pelo amor, paciência e incentivo. A Iara, minha companheira nesta longa jornada, pelo carinho inefável, incentivo e por tornar minha vida cada dia mais feliz. Ao meu orientador prof. Paulo Seleghim Júnior, pela oportunidade e confiança a mim depositada para a realização desse trabalho. À todos os meus amigos e colegas de laboratório que fizeram parte desta longa caminhada, em especial para Márcia, Evelise,
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