Zhaohui Xia^1,3, Baichuan Gao³, Chen Yu^2,*, Haotian Han³, Haobo Zhang³, Shuting Wang³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1103-1137, 2024, DOI:10.32604/cmes.2023.029177

Abstract This paper aims to solve large-scale and complex isogeometric topology optimization problems that consume significant computational resources. A novel isogeometric topology optimization method with a hybrid parallel strategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equation solving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency of CPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload between CPU and GPU. To illustrate the advantages of the proposed method, three benchmark examples are tested to verify the hybrid parallel strategy in this paper. The results… More > Graphic Abstract

Wu-Chun Chung¹, Yung-Chin Chang¹, Ching-Hsien Hsu^2,3, Chih-Hung Chang⁴, Che-Lun Hung^4,5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 351-371, 2023, DOI:10.32604/cmc.2023.035720

Abstract Federated learning is an emerging machine learning technique that enables clients to collaboratively train a deep learning model without uploading raw data to the aggregation server. Each client may be equipped with different computing resources for model training. The client equipped with a lower computing capability requires more time for model training, resulting in a prolonged training time in federated learning. Moreover, it may fail to train the entire model because of the out-of-memory issue. This study aims to tackle these problems and propose the federated feature concatenate (FedFC) method for federated learning considering heterogeneous clients. FedFC leverages the model… More >

Juan Fang^1,*, Kuan Zhou¹, Mengyuan Zhang¹, Wei Xiang^2,3

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1621-1635, 2022, DOI:10.32604/cmc.2022.027147

Abstract In recent years, with the development of processor architecture, heterogeneous processors including Center processing unit (CPU) and Graphics processing unit (GPU) have become the mainstream. However, due to the differences of heterogeneous core, the heterogeneous system is now facing many problems that need to be solved. In order to solve these problems, this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies. To improve the performance of the system, this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks. The combination strategy… More >