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

Abstract

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.

A rapid GPU-based heat transfer and solidification model for dynamic computer simulations of continuous steel casting

“…All the mentioned authors reported that the use of GPUs allowed for the acceleration of computations. The multiple of acceleration between parallel GPU-based and serial CPU-based implementations was from 2 (rather small) presented by Ansoni et al (2015) to 40 (very high) reported by Jiang et al (2015). It is therefore obvious from these results that GPUs and their use can greatly enhance the performance of computational models.…”

“…Rong et al (2014) developed a three-dimensional parallel heat transfer model for the calculation of fluid flow and heat transfer characteristics in a pipe with embedded porous media. Ansoni et al (2015) investigated the acceleration of the inverse heat transfer problem by means of parallel computing with the use of GPUs and multi-core CPUs. The authors developed a gradient method for solving sparse linear systems on GPUs.…”