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Advanced Parallel Processing Technologies 1

丛书名： Lecture Notes in computer science

1000年

作者： Jiannong Cao Wolfgang Nejdl Ming Xu

ISBN: (数字)9783540321071

ISBN: (纸本)9783540296393

Welcome to the proceedings of APPT 2005: the 6th International Workshop on Advanced Parallel Processing Technologies. APPT is a biennial workshop on parallel and distributed processing. Its scope covers all aspects of parallel and distributed computing technologies, including architectures, software systems and tools, algorithms, and applications. APPT originated from collaborations by researchers from China and Germany and has evolved to be an international workshop. APPT 2005 was the sixth in the series. The past ?ve workshops were held in Beijing, Koblenz, Changsha, Ilmenau, and Xiamen, respectively. The Program Committee is pleased to present the proceedings for APPT 2005. This year, APPT 2005 received over 220 submissions from researchers all over the world. All the papers were peer reviewed by two to three Program Committee members on their relevance, originality, signi?cance, technical qu- ity, and presentation. Based on the review result, 55 high-quality papers were selected to be included in the proceedings. The papers in this volume represent the forefront of research on parallel processing and related ?elds by researchers from China, Germany, USA, Korea, India, and other countries. The papers - cepted cover a wide range of exciting topics, including architectures, software, networking, and applications.

关键词： Software Engineering/Programming and Operating Systems computer Systems Organization and Communication Networks computer Hardware Algorithm Analysis and Problem Complexity Numeric computing Discrete Mathematics in computer science

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Computational Diffusion MRI 1

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丛书名： Mathematics and Visualization

1000年

作者： Andrea Fuster Aurobrata Ghosh Enrico Kaden Yogesh Rathi Marco Reisert

ISBN: (数字)9783319285887

ISBN: (纸本)9783319285863;9783319803814

Over the last decade interest in diffusion MRI has exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into clinical practice. New processing methods are essential for addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference.

关键词： Visualization Computational Biology/Bioinformatics Computational science and Engineering Simulation and Modeling Image Processing and computer Vision Statistics for Life sciences, Medicine, Health sciences

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Advances in Information Systems Development 1

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丛书名： Lecture Notes in Information Systems and Organisation

1000年

作者： Gheorghe Cosmin Silaghi Robert Andrei Buchmann Virginia Niculescu Gabriela Czibula Chris Barry Michael Lang Henry Linger Christoph Schneider

ISBN: (数字)9783031324185

ISBN: (纸本)9783031324178

This volume is a collection of papers on emerging concepts, significant insights, and novel approaches on information systems development (ISD). It examines and investigates up-and-coming trends in ISD in general, emphasizing benefits and risks of Artificial Intelligence in the development and operation of Information Systems. The book draws on invited papers selected from the proceedings of the 30th International Conference on Information Systems Development hosted by Babeș-Bolyai University, Cluj-Napoca, Romania, August 31 - September 2, 2022 (ISD2022).;The theme of ISD2022 was “Artificial Intelligence for Information Systems Development and Operations”. The conference focused on the interplay between Information Systems and Artificial Intelligence, trying to emphasize novel, smarter automation approaches and the mitigation of risks related to AI adoption. Primary readership of the volume are researchers interested in methodological and operational perspectives related to ISD in general, and to AI adoption as a means of digital transformation in particular.

关键词： Business Information Systems Management of computing and Information Systems computer Applications Information Systems and Communication Service

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Advances in Parallel & Distributed Processing, and Applications 1

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丛书名： Transactions on Computational science and Computational Intelligence

1000年

作者： Hamid R. Arabnia Leonidas Deligiannidis Michael R. Grimaila Douglas D. Hodson Kazuki Joe Masakazu Sekijima Fernando G. Tinetti

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Sensor Based Intelligent Robots 1

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丛书名： Lecture Notes in computer science

1000年

作者： Henrik I. Christensen Horst Bunke Hartmut Noltemeier

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Complex Networks and Their Applications VII 1

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丛书名： Studies in Computational Intelligence

1000年

作者： Luca Maria Aiello Chantal Cherifi Hocine Cherifi Renaud Lambiotte Pietro Lió Luis M. Rocha

ISBN: (数字)9783030054113

ISBN: (纸本)9783030054106

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

关键词： Computational Intelligence Artificial Intelligence

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Applications of Evolutionary Computation 1

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丛书名： Lecture Notes in computer science

1000年

作者： Cecilia Chio Anthony Brabazon Gianni A. Caro Rolf Drechsler Muddassar Farooq Jörn Grahl Gary Greenfield Christian Prins Juan Romero Giovanni Squillero Ernesto Tarantino Andrea G. B. Tettamanzi Neil Urquhart A. Şima Uyar

ISBN: (数字)9783642205200

ISBN: (纸本)9783642205194

This book constitutes the refereed proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2011, held in Torino, Italy, in April 2011 colocated with the Evo* 2011 events. Thanks to the large number of submissions received, the proceedings for EvoApplications 2011 are divided across two volumes (LNCS 6624 and 6625). The present volume contains contributions for EvoCOMNET, EvoFIN, EvoIHOT, EvoMUSART, EvoSTIM, and EvoTRANSLOC. The 51 revised full papers presented were carefully reviewed and selected from numerous submissions. This volume presents an overview about the latest research in EC. Areas where evolutionary computation techniques have been applied range from telecommunication networks to complex systems, finance and economics, games, image analysis, evolutionary music and art, parameter optimization, scheduling, and logistics. These papers may provide guidelines to help new researchers tackling their own problem using EC.

关键词： Computation by Abstract Devices Programming Techniques computer Communication Networks Math Applications in computer science Image Processing and computer Vision Artificial Intelligence

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Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things

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ACM Transactions on Internet technology 1000年

作者： Jiakun Gao Xiaolong Xu Lianyong Qi Wanchun Dou Xiaoyu Xia Xiaokang Zhou School of Software Nanjing University of Information Science and Technology NanJing China School of Software Nanjing University of Information Science and Technology NanJing China and Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) QingDao China and State Key Laboratory for Novel Software Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Technology RMIT University Victoria Australia Faculty of Data Science Shiga University Hikone Japan

The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.

关键词： Internet of Medical Things (IoMT) Mobile Edge computing (MEC) Unmanned Aerial Vehicles (UAV) Non-Orthogonal Multiple Access (NOMA)

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CGCGraph: Efficient CPU-GPU Co-execution for Concurrent Dynamic Graph Processing

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ACM Transactions on Architecture and Code Optimization 1000年

作者： Yiming Sun Jie Zhang Huawei Cao Yuan Zhang Xuejun An Junying Huang Xiaochun Ye Institute of Computing Technology Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China University of the Chinese Academy of Sciences School of Computer Science and Technology Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Shanghai Innovation Center for Processor Technologies Shanghai China

With the continuous growth of user scale and application data, the demand for large-scale concurrent graph processing is increasing. Typically, large-scale concurrent graph processing jobs need to process corresponding snapshots of dynamically changing graph data to obtain information at different time points. To enhance the throughput of such applications, current solutions concurrently process multiple graph snapshots on the GPU. However, when dealing with rapidly changing graph data, transferring multiple snapshots of concurrent jobs to the GPU results in high data transfer overhead between CPU and GPU. Additionally, the execution mode of existing work suffers from underutilization of GPU computational *** this work, we introduce CGCGraph, which can be integrated into existing GPU graph processing systems like Subway, to enable efficient concurrent graph snapshot processing jobs and enhance overall system resource utilization. The key idea is to offload unshared graph data of multiple concurrent snapshots to the CPU, reducing CPU-GPU transfer overhead. By implementing CPU-GPU co-execution, there is potential for enhanced utilization of GPU computing resources. Specifically, CGCGraph leverages kernel fusion to process shared graph data concurrently on the GPU, while executing all snapshots in parallel on the CPU, with each snapshot assigned a dedicated thread. This approach enables efficient concurrent processing within a novel CPU-GPU co-execution model, incorporating three optimization strategies targeting storage, computation, and synchronization. We integrate CGCGraph with Subway, an existing system designed for out-of-GPU-memory static graph processing. Experimental results show that the integration of CGCGraph with current GPU-based systems obtains performance improvements ranging from 1.7 to 4.5 times.

关键词： CPU-GPU co-execution concurrent graph processing dynamic graph snapshot processing high throughput

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Cheetah: Accelerating Dynamic Graph Mining with Grouping Updates

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ACM Transactions on Architecture and Code Optimization 1000年

作者： Yi Zhang Xiaomeng Yi Yu Huang Jingrui Yuan Chuangyi Gui Dan Chen Long Zheng Jianhui Yue Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Software Engineering Huazhong University of Science and Technology Wuhan China Eastern Institute of Technology Ningbo China National University of Singapore Singapore Singapore Michigan Technological University Houghton United States School of Computer Science and Engieering UNSW Sydney Kensington Australia

Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods use fine-grained incremental computation to avoid full re-mining after each update, which improves speed but often overlooks potential gains from examining inter-update interactions holistically, thus missing out on overall efficiency *** this paper, we introduce Cheetah, a dynamic graph mining system that processes updates in a coarse-grained manner by leveraging exploration domains. These domains exploit the community structure of real-world graphs to uncover data reuse opportunities typically missed by existing approaches. Exploration domains, which encapsulate extensive portions of the graph relevant to updates, allow multiple updates to explore the same regions efficiently. Cheetah dynamically constructs these domains using a management module that identifies and maintains areas of redundancy as the graph changes. By grouping updates within these domains and employing a neighbor-centric expansion strategy, Cheetah minimizes redundant data accesses. Our evaluation of Cheetah across five real-world datasets shows it outperforms current leading systems by an average factor of 2.63 ×.

关键词： Graph mining system dynamic graph incremental computing

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