Constructing and analyzing algorithms of tensor operation paralleling by Anatoliy Nikolayevich Makokha, Tat'yana Yevgen'yevna Tyshlyar

“…It is assumed that the time of execution for all computing operations is same and equal to a unit of time, and data transfer between computer devices is performed instantaneously without any time consuming (it is acceptable, for example, a parallel computing systems with shared memory). In particular, it is shown that for the tensors' addition the time of the fastest execution of algorithm for an unlimited number of processors is equal to the length of the maximum path in the graph. …”
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Identifying Carbon Star Candidates from LAMOST DR11 and SDSS DR18 Based on a Parallel Feature Recognition Method by Lichan Zhou, Jianghui Cai, Haifeng Yang, Ali Luo, Yinbi Li, Chenhui Shi, Boyu Zhang, Jianing Tian, Yinan Yuan, Yuqing Yang

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Maximum Mutual Information Feature Extraction Method Based on the Cloud Platform by Shasha Wei, Huijuan Lu, Wei Jin, Chao Li

“…With the large-scale application of gene chip,gene expression data with high dimension which exists a large number of irrelevant and redundant features may reduce classifier performance problem.A maximum mutual information feature extraction method based on cloud platforms was proposed.Hadoop cloud computing platform could be a parallel computing after gene expression data segmentation,features was extracted at the same time combined with the maximum mutual information method and the characteristics of cloud computing platform filter model was realized.Simulation experiments show that the maximum mutual information feature extraction method based on the cloud platform can rapid extraction of features in a higher classification accuracy which save a lot of time resources to make a highly efficient gene feature extraction system.…”
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OBJECT TRACKING VIA COMPARISON OF COLOR HISTOGRAMS by B. A. Zalesky, E. N. Seredin, M. V. Yadlouski

“…The histograms are compared by the Bhattacharia criterion. А parallel computing platform CUDA, developed to program GPUs, allows creation of real time or near-real time program realizations of the offered versions. …”
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On the optimal layout of (K p  − C p ) n into grid and certain structures by G. Caroline Vincy, David Raj Micheal

“…Abstract Interconnection networks constitute complex configurations of processors and communication links that facilitate data transmission between processors in a parallel computing system. Their architecture and design heavily depend on parameters such as wirelength, dilation, bandwidth, and minimum cutwidth. …”
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Building accurate numerical models by Sharapov Dmitry

“…Key strategies such as algorithm optimization, parallel computing, and efficient data management are essential for maximizing computational resources. …”
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Efficient and real-time lane detection using CUDA-based implementation by El Boussaki Hoda, Latif Rachid, Saddik Amine

“…To this end, we used CUDA for acceleration, taking advantage of its parallel computing capabilities to improve performance. …”
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Chopin: An open source R-language tool to support spatial analysis on parallelizable infrastructure by Insang Song, Kyle P. Messier

“…Supporting popular R spatial-analysis libraries, chopin exploits parallel computing by partitioning data involved in each task. …”
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Parallel Implementation of Katsevich's FBP Algorithm by Jiansheng Yang, Xiaohu Guo, Qiang Kong, Tie Zhou, Ming Jiang

“…For spiral cone-beam CT, parallel computing is an effective approach to resolving the problem of heavy computation burden. …”
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Survey of FPGA based recurrent neural network accelerator by Chen GAO, Fan ZHANG

“…Recurrent neural network(RNN) has been used wildly used in machine learning field in recent years,especially in dealing with sequential learning tasks compared with other neural network like CNN.However,RNN and its variants,such as LSTM,GRU and other fully connected networks,have high computational and storage complexity,which makes its inference calculation slow and difficult to be applied in products.On the one hand,traditional computing platforms such as CPU are not suitable for large-scale matrix operation of RNN.On the other hand,the shared memory and global memory of hardware acceleration platform GPU make the power consumption of GPU-based RNN accelerator higher.More and more research has been done on the RNN accelerator of the FPGA in recent years because of its parallel computing and low power consumption performance.An overview of the researches on RNN accelerator based on FPGA in recent years is given.The optimization algorithm of software level and the architecture design of hardware level used in these accelerator are summarized and some future research directions are proposed.…”
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Numerical Solution of Diffusion Models in Biomedical Imaging on Multicore Processors by Luisa D'Amore, Daniela Casaburi, Livia Marcellino, Almerico Murli

“…This analysis is carried out in a multicore-based parallel computing environment.…”
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Pengukuran Performa Apache Spark dengan Library H2O Menggunakan Benchmark Hibench Berbasis Cloud Computing by Aminudin Aminudin, Eko Budi Cahyono

“…This concept is called parallel computing. Apache Spark has advantages compared to other similar frameworks such as Apache Hadoop, etc., where Apache Spark is able to process data in streaming, meaning that the data entered into the Apache Spark environment can be directly processed without waiting for other data to be collected. …”
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Deployment and Application of On-Board PHM Models Based on Docker Containers for Intelligent Operation and Maintenance by DU Jiwei, WEN Lin, LYU Yu, JIANG Guotao, CHEN Kai, XIONG Yukai

“…Additionally, the parallel computing efficiency of multiple models is improved due to the lack of data interactions among them, facilitating superior portability and scalability.…”
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Deep learning-based multimodal trajectory prediction methods for autonomous driving: state of the art and perspectives by Jun HUANG, Yonglin TIAN, Xingyuan DAI, Xiao WANG, Zhixing PING

“…Although deep learning methods have achieved better results than traditional trajectory prediction algorithms, there are still problems such as information loss, interaction and uncertainty difficulties in modelling, and lack of interpretability of predictions when implementing multimodal high-precision prediction for autonomous vehicles in heterogeneous, highly dynamic and complex changing environments.The newly developed Transformer's long-range modelling capability and parallel computing ability make it a great success not only in the field of natural language processing, but also in solving the above problems when extended to the task of multimodal trajectory prediction for autonomous driving.Based on this, the aim of this paper is to provide a comprehensive summary and review of past deep neural network-based approaches, in particular the Transformer-based approach.The advantages of Transformer over traditional sequential network, graphical neural network and generative model were also analyzed and classified in relation to existing challenges, simultaneously.Transformer models can be better applied to multimodal trajectory prediction tasks, and that such models have better generalisation and interpretability.Finally, the future directions of multimodal trajectory prediction were presented.…”
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Static Scheduling of Periodic Hardware Tasks with Precedence and Deadline Constraints on Reconfigurable Hardware Devices by Ikbel Belaid, Fabrice Muller, Maher Benjemaa

“…Using these three main stages, dynamic partial reconfiguration and mixed integer programming, pipelined scheduling and efficient placement are achieved and enable parallel computing of the task graph on the reconfigurable devices by optimizing placement/scheduling quality. …”
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Computational Modeling of Ganglion Cell Bicolor Opponent Receptive Fields and FPGA Adaptation for Parallel Arrays by Hui Wei, Wenbo Yao

“…We also implement a circuit-level distributed parallel computing model on FPGAs. The results show that we are able to transfer information with low energy consumption and high parallelism. …”
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Data Mining for the Internet of Things: Literature Review and Challenges by Feng Chen, Pan Deng, Jiafu Wan, Daqiang Zhang, Athanasios V. Vasilakos, Xiaohui Rong

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Optimizing Euclidean Distance Computation by Rustam Mussabayev

“…From spatial data structures and approximate nearest neighbor algorithms to dimensionality reduction, vectorization, and parallel computing, various approaches exist to accelerate Euclidean distance computation in different contexts. …”
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A Domain Decomposition Method for Time Fractional Reaction-Diffusion Equation by Chunye Gong, Weimin Bao, Guojian Tang, Yuewen Jiang, Jie Liu

“…The computational complexity of one-dimensional time fractional reaction-diffusion equation is O(N2M) compared with O(NM) for classical integer reaction-diffusion equation. Parallel computing is used to overcome this challenge. Domain decomposition method (DDM) embodies large potential for parallelization of the numerical solution for fractional equations and serves as a basis for distributed, parallel computations. …”
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Solving the SAT Problem by Cell-Like P Systems with Channel States and Symport Rules by Xiaoming Wan, Chuchuan Liu, Yueguo Luo

“…Cell-like P systems with channel states, which are a variant of tissue P systems in membrane computing, can be viewed as highly parallel computing devices based on the nested structure of cells, where communication rules are classified as symport rules and antiport rules. …”
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