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Benchmarking and Extending SYCL Hierarchical Parallelism 2

Benchmarking and Extending SYCL Hierarchical Parallelism

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2nd Workshop on Hierarchical Parallelism for Exascale Computing, HiPar 2021

作者： Deakin, Tom McIntosh-Smith, Simon Alpay, Aksel Heuveline, Vincent University of Bristol Department of Computer Science Bristol United Kingdom Universität Heidelberg Engineering Mathematics and Computing Lab and Interdisciplinary Center for Scientific Computing Heidelberg Germany

ISBN: (纸本)9781665411325

SYCL is an open-standard, parallel programming model for programming heterogeneous devices from Khronos. It allows single-source programming of diverse attached devices in a cross-platform manner in modern C++. SYCL provides different layers of parallel abstractions, including Same Instruction Multiple Thread (SIMT) kernels, data-parallel loop concurrency and hierarchical parallelism. We discuss Scoped Parallelism as an extension to the existing Hierarchical Parallelism in SYCL, and highlight the advantages and disadvantages of these models from the perspective of the programmer and an implementer of SYCL. In this paper, we compare writing benchmark programs using SIMT kernel, hierarchical parallelism and scoped parallelism paradigms, and present results running on a high-performance CPU and GPU. © 2021 IEEE.

关键词： Benchmarking

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Re-parameterizable Residual Multiple Convolutions Network for Efficient Single Image Super-Resolution 2

Re-parameterizable Residual Multiple Convolutions Network fo...

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2nd international conference on Frontiers of artificial Intelligence and Machine Learning, FAIML 2023

作者： Zhang, Qiong Yin, Zhengnan Kuang, Yonghai Shantou University Medical College China Shantou University College of Engineering China

ISBN: (纸本)9798400707544

The performance of single image super-resolution (SISR) has improved significantly in recent years. However, most existing methods suffer from large model parameters, high computational costs, and slow inference times, making them unsuitable for resource-constrained devices. To optimize these limitations, we propose a new re-parameterizable residual multiple convolutions network (RepRMCN) that strikes a better balance between model performance, scale, and inference time. Our approach introduces a convolutional reparameterization strategy that combines multiple convolutionals into a single convolution after model training, improving model performance during training and reducing the parameters and inference time when deployed. We also designed a simple grouped structure with different convolutions that inspired by grouped convolution, Inception, and heterogeneous kernel-based convolution. Our proposed structure splits features into two groups and processes them using re-parameterizable multiple convolutions module and dilation convolution, respectively. Experimental results show that RepRMCN outperforms existing high-efficiency SR models in terms of parameter reduction and inference time while maintaining good performance. Specifically, when tested on the Urban100 dataset with a 4× scale factor, our RepRMCN has 495K parameters, which is 48K less than the current state-of-the-art model RLFN. The inference time on the GeForce GTX 1080Ti is 16.47ms, which is comparable to RLFN. © 2023 Copyright held by the owner/author(s).

关键词： Convolution

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Gaussian Function Representation of 2nd-Order Normal Cloud Model

Gaussian Function Representation of 2nd-Order Normal Cloud M...

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international conference on Intelligent System and Knowledge engineering, ISKE

作者： Min Li Guoyin Wang Zeng Yu Hongjun Wang Jihong Wan Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Guangdong University of Technology Guangdong China

Natural language is a fruit of human cognition, and its basic units are linguistic terms. Linguistic terms' representation and processing have played a fundamental role in applications such as risk assessment. Cloud Model, which integrates the approaches of probability theory and fuzzy sets, is a model for expressing the uncertainties of linguistic terms. Its advantage is that it is a bidirectional cognitive model. The 2nd-order normal cloud (2Ord-NC) is a specific Cloud Model. It can represent the linguistic concept's intension using three statistics parameters (Expectation Ex, Entropy En, and Hyper-entropy He) and its extension using cloud drops (instances). A cognitive transformation between a qualitative concept and its quantitative instances can be implemented by probabilistic programming (FCT and BCT). In the current works, Upper and Lower membership functions characterize Cloud Model-s. However, scholars have yet to explain this representation method's rationality theoretically. This paper proposes the 2nd-order normal cloud model's Gaussian function representation. Firstly, it is proved that the 2nd-order normal cloud model (2Ord-NCM) can be determined by its outer envelope and expectation membership functions. The properties of the 2Ord-NCM's fuzzy membership functions are analyzed when the concept is clear and unclear, and the definitions of inner envelope membership function family, outer envelope membership function family, and representative membership function family are given. Secondly, it is proved that the 2Ord-NCM can also be determined by any two of its representative membership functions. This work enriches the Cloud Model's mathematical theoretical foundations, thereby better guiding its practical application.

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SOP Planning of Power Distribution System Based on Source-load Cluster Division

SOP Planning of Power Distribution System Based on Source-lo...

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Power science and Technology (ICPST), IEEE international conference on

作者： Tao Li Xinghang Weng Baojun Xu Changhao He Rui Qin Yanhe Yin Yihan Sun Shaohan Lu Zhongshan Power Supply Bureau Guangdong Power Grid Co. Ltd Zhongshan China Power Grid Planning Center Guangdong Power Grid Co. Ltd. Guangzhou China The School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

A robust planning model based on source-load cluster division is proposed to address the limitations of the current soft open point (SOP) in replacing only the alternative positions of the tie switches and the dimension disaster caused by arbitrary access node planning. This model is used to optimize the selection of soft switch access positions and capacities in the distribution network. Firstly, a comprehensive cluster division method for multi-objective distribution network is proposed, considering modularity, intra-cluster net power fluctuation, and net power peak fluctuation. Secondly, based on the cluster division, a robust planning model for soft switch distribution is established, with the objective of minimizing the annual comprehensive cost of the distribution network. Thirdly, the second-order cone programming model is transformed into a mixed-integer linear programming model using techniques such as equivalent substitution, polyhedral linearization, and Big-M method. The column and constraint generation algorithm (C&CG) is used to solve the proposed model. Finally, a case study of the distribution system of Taiwan Power Company (TPC) is used to validate the effectiveness of the proposed model, ensuring the economic feasibility of the distribution network planning and construction scheme. According to the calculations, the proposed method can reduce the O&M costs of the distribution system by 23.6% compared to the scenario without planning. Meanwhile, compared to the traditional planning method, the proposed method can reduce the cost of distribution system planning by 5.23%.

关键词： Costs Fluctuations Uncertainty Soft switching Power distribution Distribution networks programming

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Design of Interactive Computer Algorithm Learning Platform : Taking the visual programming tool 'Progressive Blockly' as an example 2

Design of Interactive Computer Algorithm Learning Platform :...

引用

2nd international conference on Computer engineering and Application, ICCEA 2021

作者： Xia, Zhenghua Hu, Baokun Diao, Wanwan Huang, Yuqian Hangzhou Normal University School of Information Science and Engineering Hangzhou China

ISBN: (纸本)9781665426169

The traditional computer algorithm course is boring, and it is difficult for students to fully digest and absorb the algorithm knowledge. Therefore, it is of great significance to decompose the algorithm steps and visualize the algorithm process so as to learn the algorithm interactively. Firstly, this paper makes a comparative analysis of the existing visual algorithm learning platforms at home and abroad, and points out the necessity of developing the interactive computer algorithm learning platform. Secondly, it describes the main functions and system architecture of the platform, followed by an introduction to the realization of the key technologies of the platform. Finally, the innovation points-the realization of C language module, the real-Time generation and display of blocks corresponding codes, the algorithm animation demonstration and so on are described in detail, and the future development of the platform is prospected. © 2021 IEEE.

关键词： Learning algorithms

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UJN-SAR: A Large Dataset with Experimental Analysis for Water Body Segmentation from SAR Images 2nd

UJN-SAR: A Large Dataset with Experimental Analysis for Wate...

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2nd international conference on Image, Vision and Intelligent Systems, ICIVIS 2022

作者： Li, Wenshuo Xu, Tao Wang, Yulin Yang, Xiaohui Shen, Yuan Li, Yan Yu, Kunfeng School of Information Science and Engineering University of Jinan Jinan250022 China CHEOS Shangdong Center of Data and Application University of Jinan Jinan250022 China Institute of Satellite Technology Shandong Institute of Industrial Technology Jinan China

ISBN: (纸本)9789819909223

Water segmentation from SAR images plays a very important role in water resources investigation, ecological evaluation and flood response. In recent years, convolutional neural network based method has received much attention in target segmentation from remote sensing image, especially in water body segmentation of SAR image. However, limited imaging quality and complex backgrounds bring challenges for water body segmentation, and insufficient labeled samples prevents deep learning based segmentation algorithms from achieving satisfied performance. For further comprehensive research, a pure large SAR water segmentation dataset, named UJN-SAR, was proposed in this paper, which was constructed based on domestic SAR satellite data and has advantages in imaging quality and various spatial resolution. Furthermore, detailed comprehensive experimental analysis was presented based on UJN-SAR via state of the art segmentation algorithms. Experimental results illustrated the effectiveness of the dataset, which will be a benchmark dataset for SAR-based water segmentation, and the models with parameters obtained in the experiment can also be utilized as initial weights of the pre-trained segmentation models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

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Research in Big Data Analytics Utilizing Simulations

Research in Big Data Analytics Utilizing Simulations

引用

Applied artificial Intelligence and Computing (ICAAIC), international conference on

作者： P. Vignesh S. Srinivasan S. Maragathasundari K. Karthikeyan C. Swedheetha M. Kameswari Department of Mathematics B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai Tamilnadu India Department of Mathematics Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Anna university regional campus Madurai Tamilnadu India Department of Mathematics Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

Using simple programming approaches, the Apache Hadoop software facilitates the processing of massive data streams by a multitude of processors deployed across clusters of devices using straightforward programming techniques. One single computer will be replaced in the future by tens of billions of devices, each with a unique capacity for processing data. The library is designed to recognize and handle protocol stack failures rather than relying on hardware for automated failover, enabling a massively scalable service to be offered on top of numerous servers that might all fail. Physically, it is impossible to fully implement the existing error-prone design technique. Thus, the queueing theory is used to assess the strategy used in this design. The supplemental variable strategy is used to solve the queuing issue that results from the afore-mentioned. For various execution strategies, calculations are done on the queue size, server idle time, server utilisation, and probability generating methodologies. Numerical analysis was used to explore a number of unusual circumstances. Because of this procedure's actual application and statistical separation, it is completely legal. The graphical representation of this point of view gives a brief reading of the proposed borders.

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Research on the Application of Computer Big Data Algorithms in artificial Intelligence Technology 2021

Research on the Application of Computer Big Data Algorithms ...

引用

2nd international conference on Computing and Data science, CONF-CDS 2021

作者： Liu, Hongying Department of Computer Science and Engineering Guangzhou College of Technology and Business Guangzhou510850 China

ISBN: (纸本)9781450389570

The paper takes the realization process of an artificial intelligence speech recognition system that can recognize numbers 0-9 as an example, and elaborates the basic principles and key technologies of isolated words speech recognition for specific people based on DTW big data algorithm. It includes a detailed discussion on the voice endpoint detection method, the characteristic parameter calculation method and the realization of the DTW big data algorithm. Finally, the programming method and experimental results under MATLAB are given. © 2021 ACM.

关键词： Speech recognition

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Advancing Breast Cancer Prediction Using Logistic Regression and Machine Learning Techniques

Advancing Breast Cancer Prediction Using Logistic Regression...

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Sustainable Computing and Smart Systems (ICSCSS), international conference on

作者： Ruchika Bhuria Kanwarpartap Singh Gill Sonal Malhotra Mukesh Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The second leading cause of death global is cancer. In 2015, 8.8 million people lost their lives to cancer. The most common cause of mortality for women is breast cancer. Research on early identification of breast cancer to initiate therapy and improve survival rates has been conducted in a number of ways. Breast cancer is a diverse illness with several molecular kinds and forms. Breast cancer is the only kind of cancer that affects women worldwide, and it may be a common cause of death. This paper's main goal is to develop a model for predicting breast cancer utilizing several machines. Moreover, compare and assess the recall, accuracy, precision, f1-Score, and support performance of the different classifiers. This study proposes a machine learning (ML) approach to identify patients with triple negative breast cancer from those without it by using gene expression data. Among the many techniques machine learning researchers have been using to improve accuracy is precisely classifying samples that belong to the majority class while ignoring those that belong to the minority class. Teaching computers to learn and work without clear programming or instructions is known as machine learning. Then, the learned data is applied to predict the likelihood of breast cancer. Furthermore, the datasets used for breast cancer diagnosis and detection is used. Images and blood among other data kinds can be employed with the proposed model. The prediction outcomes show how well machine learning algorithms work and how they may be used to distinguish between triple negative and non-triple negative breast cancer.

关键词： Logistic regression Accuracy Computational modeling Medical treatment Machine learning Predictive models programming

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Re2GCC: Retrieve-Rerank-Generate Framework for Code Completion

Re2GCC: Retrieve-Rerank-Generate Framework for Code Completi...

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artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR), international conference on

作者： Siying Du Qiming Wang Danping Jia School of Information Science and Engineering Shenyang University of Technology Shenyang China

Code pre-trained language models such as CodeGPT have demonstrated very promising results on the code completion task. The retrieval-augmented approach has further improved the accuracy of code completion. Previous retrieval-augmented approaches require a code-to-code retriever, but there is no standard dataset for retriever training, which usually requires manual construction of a training dataset containing semantically similar code pairs, and this is time-consuming and resource-intensive. We propose Re 2 GCC, a retrieve-rerank-generate framework for code completion. This framework obtains the most relevant code fragments to the unfinished code in the zero-shot setting by combining sparse retrieval and reranking techniques, which are then combined with an auto-regressive language model for generation. Compared to previous approaches, Re 2 GCC provides a low-cost solution for retrieval-augmented code completion tasks. Evaluating on the Python programming language, we demonstrate the superior performance of the Re 2 GCC framework on the code completion task.

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