Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights t...Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.展开更多
We proposed an improved graphics processing unit(GPU)acceleration approach for three-dimensional structural topology optimization using the element-free Galerkin(EFG)method.This method can effectively eliminate the ra...We proposed an improved graphics processing unit(GPU)acceleration approach for three-dimensional structural topology optimization using the element-free Galerkin(EFG)method.This method can effectively eliminate the race condition under parallelization.We established a structural topology optimization model by combining the EFG method and the solid isotropic microstructures with penalization model.We explored the GPU parallel algorithm of assembling stiffness matrix,solving discrete equation,analyzing sensitivity,and updating design variables in detail.We also proposed a node pair-wise method for assembling the stiffnessmatrix and a node-wise method for sensitivity analysis to eliminate race conditions during the parallelization.Furthermore,we investigated the effects of the thread block size,the number of degrees of freedom,and the convergence error of preconditioned conjugate gradient(PCG)on GPU computing performance.Finally,the results of the three numerical examples demonstrated the validity of the proposed approach and showed the significant acceleration of structural topology optimization.To save the cost of optimization calculation,we proposed the appropriate thread block size and the convergence error of the PCG method.展开更多
文摘Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.
基金This work is supported by the National Natural Science Foundation of China(Nos.51875493,51975503,11802261)The financial support to the first author is gratefully acknowledged.
文摘We proposed an improved graphics processing unit(GPU)acceleration approach for three-dimensional structural topology optimization using the element-free Galerkin(EFG)method.This method can effectively eliminate the race condition under parallelization.We established a structural topology optimization model by combining the EFG method and the solid isotropic microstructures with penalization model.We explored the GPU parallel algorithm of assembling stiffness matrix,solving discrete equation,analyzing sensitivity,and updating design variables in detail.We also proposed a node pair-wise method for assembling the stiffnessmatrix and a node-wise method for sensitivity analysis to eliminate race conditions during the parallelization.Furthermore,we investigated the effects of the thread block size,the number of degrees of freedom,and the convergence error of preconditioned conjugate gradient(PCG)on GPU computing performance.Finally,the results of the three numerical examples demonstrated the validity of the proposed approach and showed the significant acceleration of structural topology optimization.To save the cost of optimization calculation,we proposed the appropriate thread block size and the convergence error of the PCG method.
