Wei Wang^1,2, Yanfeng Zheng^1,3, Jingzhe Tang¹, Chao Yang¹, Yaozhi Luo^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 595-626, 2021, DOI:10.32604/cmes.2021.017321

Abstract Large deformation contact problems generally involve highly nonlinear behaviors, which are very time-consuming and may lead to convergence issues. The finite particle method (FPM) effectively separates pure deformation from total motion in large deformation problems. In addition, the decoupled procedures of the FPM make it suitable for parallel computing, which may provide an approach to solve time-consuming issues. In this study, a graphics processing unit (GPU)-based parallel algorithm is proposed for two-dimensional large deformation contact problems. The fundamentals of the FPM for planar solids are first briefly introduced, including the equations of motion of particles and the internal forces of… More >

M. Usman Ashraf^1,*, Fathy Alburaei Eassa², Leon J. Osterweil³, Aiiad Ahmad Albeshri², Abdullah Algarni², Iqra Ilyas⁴

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 615-639, 2021, DOI:10.32604/iasc.2021.019044

Abstract High Performance Computing (HPC) technologies are emphasizing to increase the system performance across many disciplines. The primary challenge in HPC systems is how to achieve massive performance by minimum power consumption. However, the modern HPC systems are configured by adding the powerful and energy efficient multi-cores/many-cores parallel computing devices such as GPUs, MIC, and FPGA etc. Due to increasing the complexity of one chip many-cores/multi-cores systems, only well-balanced and optimized parallel programming technique is the solution to provide substantial increase in performance under power consumption limitations. Conventionally, the researchers face various barriers while parallelizing their serial code because they don’t… More >

Yang Zhou¹, Jinhui He¹, Hao Yang^1,2,*

Journal of New Media, Vol.3, No.2, pp. 63-72, 2021, DOI:10.32604/jnm.2021.018829

Abstract In recent years, more and more attention has been paid to the research and application of graph structure. As the most typical representative of graph structure algorithm, breadth first search algorithm is widely used in many fields. However, the performance of traditional serial breadth first search (BFS) algorithm is often very low in specific areas, especially in large-scale graph structure traversal. However, it is very common to deal with large-scale graph structure in scientific research. At the same time, the computing performance of supercomputer has also made great progress. China’s self-developed supercomputer system Sunway TaihuLight (SW) has won the top… More >

Ahmed A. M. Jamel^1,∗, Bahriye Akay^2,†

Computer Systems Science and Engineering, Vol.35, No.6, pp. 441-456, 2020, DOI:10.32604/csse.2020.35.441

Abstract Recently, owing to the capability of mobile and wearable devices to sense daily human activity, human activity recognition (HAR) datasets have become a large-scale data resource. Due to the heterogeneity and nonlinearly separable nature of the data recorded by these sensors, the datasets generated require special techniques to accurately predict human activity and mitigate the considerable heterogeneity. Consequently, classic clustering algorithms do not work well with these data. Hence, kernelization, which converts the data into a new feature vector representation, is performed on nonlinearly separable data. This study aims to present a robust method to perform HAR data clustering to… More >

Ahmed A. M. Jamel^1,∗, Bahriye Akay^2,†

Computer Systems Science and Engineering, Vol.34, No.5, pp. 259-281, 2019, DOI:10.32604/csse.2019.34.259

Abstract Parallel processing has turned into one of the emerging fields of machine learning due to providing consistent work by performing several tasks simultaneously, enhancing reliability (the presence of more than one device ensures the workflow even if some devices disrupted), saving processing time and introducing low cost and high-performance computation units. This research study presents a survey of parallel K-means and Fuzzy-c-means clustering algorithms based on their implementations in parallel environments such as Hadoop, MapReduce, Graphical Processing Units, and multi-core systems. Additionally, the enhancement in parallel clustering algorithms is investigated as hybrid approaches in which K-means and Fuzzy-c-means clustering algorithms… More >

Jie Xu^{1, *}, Qun Wang¹, Yifan Wang¹, Khan Asif²

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1819-1844, 2020, DOI:10.32604/cmc.2020.010727

Abstract Host cardinality estimation is an important research field in network management and network security. The host cardinality estimation algorithm based on the linear estimator array is a common method. Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host. This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy. The cardinality estimating algorithm is a kind of random algorithm, and there is a deviation between the estimated results and the actual cardinalities. The deviation is affected by some systematical factors,… More >

Lili He^{1, 2}, Zhiwei Cai^{1, 2}, Dantong Ouyang^{1, 2}, Changshuai Wang^{1, 2}, Yu Jiang^{1, 2}, Chong Wang^{1, 2, 3}, Hongtao Bai^{1, 2, *}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 373-388, 2020, DOI:10.32604/cmc.2020.09860

Abstract In view of the satellite cloud-derived wind inversion has the characteristics of large scale, intensive computing and time-consuming serial inversion algorithm is very difficult to break through the bottleneck of efficiency. We proposed a parallel acceleration scheme of cloud-derived wind inversion algorithm based on MPI cluster parallel technique in this paper. The divide-and-conquer idea, assigning winds vector inversion tasks to each computing unit, is identified according to a certain strategy. Each computing unit executes the assigned tasks in parallel, namely divide-and-rule the inversion task, so as to reduce the efficiency bottleneck of long inversion time caused by serial time accumulation.… More >

Jingzhe Tang¹, Yanfeng Zheng¹, Chao Yang¹, Wei Wang¹, Yaozhi Luo^{1, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 5-31, 2020, DOI:10.32604/cmes.2020.08104

Abstract As a novel kind of particle method for explicit dynamics, the finite particle method (FPM) does not require the formation or solution of global matrices, and the evaluations of the element equivalent forces and particle displacements are decoupled in nature, thus making this method suitable for parallelization. The FPM also requires an acceleration strategy to overcome the heavy computational burden of its explicit framework for time-dependent dynamic analysis. To this end, a GPU-accelerated parallel strategy for the FPM is proposed in this paper. By taking advantage of the independence of each step of the FPM workflow, a generic parallelized computational… More >

Yanfen Guo^1,2, Zhe Cui^1,*, Zhipeng Yang³, Xi Wu², Shaahin Madani⁴

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1205-1215, 2019, DOI:10.32604/cmc.2019.06029

Abstract Since the spatial resolution of diffusion weighted magnetic resonance imaging (DWI) is subject to scanning time and other constraints, its spatial resolution is relatively limited. In view of this, a new non-local DWI image super-resolution with joint information method was proposed to improve the spatial resolution. Based on the non-local strategy, we use the joint information of adjacent scan directions to implement a new weighting scheme. The quantitative and qualitative comparison of the datasets of synthesized DWI and real DWI show that this method can significantly improve the resolution of DWI. However, the algorithm ran slowly because of the joint… More >

Min Ki Kim¹, Seung Jo Kim²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.12, No.2, pp. 52-52, 2009, DOI:10.3970/icces.2009.012.052

Abstract A new concept of hybrid iterative-multifrontal linear solution method is presented for large scale structural analysis problems. Multifrontal solution method is the best direct solution method ever known, so it is adequate for Domain decompostion types of iterative solvers. Multifrontal solver is served as an internal subdomain solver for domain decomposition iterative solver for achieving high performance of domain decomposition iterative solver. Lagrange multiplier is introduced to enforce the continuity of interface between subdomains. And corner DOF is introduced to avoid singularities of subdomains. Hybrid solution method is expected to show good parallel performance for extremely large size structural analysis. More >