Parallel computing techniques have been introduced into digital image correlation(DIC) in recent years and leads to a surge in computation speed. The graphics processing unit(GPU)-based parallel computing demonstrated...Parallel computing techniques have been introduced into digital image correlation(DIC) in recent years and leads to a surge in computation speed. The graphics processing unit(GPU)-based parallel computing demonstrated a surprising effect on accelerating the iterative subpixel DIC, compared with CPU-based parallel computing. In this paper, the performances of the two kinds of parallel computing techniques are compared for the previously proposed path-independent DIC method, in which the initial guess for the inverse compositional Gauss-Newton(IC-GN) algorithm at each point of interest(POI) is estimated through the fast Fourier transform-based cross-correlation(FFT-CC) algorithm. Based on the performance evaluation, a heterogeneous parallel computing(HPC) model is proposed with hybrid mode of parallelisms in order to combine the computing power of GPU and multicore CPU. A scheme of trial computation test is developed to optimize the configuration of the HPC model on a specific computer. The proposed HPC model shows excellent performance on a middle-end desktop computer for real-time subpixel DIC with high resolution of more than 10000 POIs per frame.展开更多
文摘引入了一种二元Lattice Boltzmann Model(LBM),实现了两种液体组成的混合流的模拟.不同于其它的类似模型,它区分考虑了流体的粘性和扩散特性,可以很容易地模拟各种互溶或者不互溶的混合流现象.此外,由于LBM的运算大都是线性的局部运算,这使得它很容易在可编程图形处理器(Graphics Process Unit,GPU)上进行加速,从而进行实时模拟.给出了若干二元混合流的模拟结果.
基金supported by the National Natural Science Foundation of China(Grant Nos.11772131,11772132,11772134&11472109)the Natural Science Foundation of Guangdong Province,China(Grant Nos.2015A030308017,2015A030311046&2015B010131009)+2 种基金the Opening fund of State Key Laboratory of Nonlinear Mechanics(LNM)CASthe State Key Lab of Subtropical Building Science,South China University of Technology(Grant Nos.2014ZC17&2017ZD096)
文摘Parallel computing techniques have been introduced into digital image correlation(DIC) in recent years and leads to a surge in computation speed. The graphics processing unit(GPU)-based parallel computing demonstrated a surprising effect on accelerating the iterative subpixel DIC, compared with CPU-based parallel computing. In this paper, the performances of the two kinds of parallel computing techniques are compared for the previously proposed path-independent DIC method, in which the initial guess for the inverse compositional Gauss-Newton(IC-GN) algorithm at each point of interest(POI) is estimated through the fast Fourier transform-based cross-correlation(FFT-CC) algorithm. Based on the performance evaluation, a heterogeneous parallel computing(HPC) model is proposed with hybrid mode of parallelisms in order to combine the computing power of GPU and multicore CPU. A scheme of trial computation test is developed to optimize the configuration of the HPC model on a specific computer. The proposed HPC model shows excellent performance on a middle-end desktop computer for real-time subpixel DIC with high resolution of more than 10000 POIs per frame.
