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2007 33rd European Conference and Exhibition of Optical Communication, ECOC 2007

作者： Wang, Zhengyu Guo, Wei Sun, Zhenyu Jin, Yaohui Sun, Weiqiang Hu, Weisheng Lin, Xinhua Wu, Min-You Liu, Hong Fu, San Yuan, Jun Qiao, Chunming State Key Lab. on Fiber-Optic Local Area Networks and Advanced Optical Communication systems Shanghai JiaoTong University Shanghai200240 China Grid Computing Center Shanghai JiaoTong University Shanghai200240 China Institute of Aerospace Science and Technology Shanghai JiaoTong University Shanghai200240 China Shanghai Supercomputer Center Shanghai201203 China Computer Science and Engineering State University of New York BuffaloNY14260 United States

One task-flow scheduler has been designed to optimally allocate resources for a task-flow based Aircraft Collab.rative Design Application in Optical grid. This scheduler and application has been deployed on an Optical... 详细信息

ISBN: (纸本)9783800730421

关键词： Scheduling

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Knowledge granulation in interval-valued information systems

Knowledge granulation in interval-valued information systems

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International Workshop on Intelligent Systems and Applications

作者： Zhang, Nan Miao, Duoqian Gao, Can Department of Computer Science and Technology Tongji University Shanghai 201804 China Department of Computer Science University of Regina Regina SK S4S 0A2 Canada Key Lab. of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G4 Canada

ISBN: (纸本)9781424498574

Rough set theory, proposed by Pawlak, has been proved to be a mathematical tool to deal with vagueness and uncertainty in intelligent information processing. In this paper, we propose the concept of knowledge granulation in interval-valued information systems, and discuss some important properties. From these properties, it can be shown that the proposed knowledge granulation provides important approaches to measuring the discernibility of different knowledge. It may be helpful for rule evaluation and knowledge discovery in interval-valued information systems. © 2011 IEEE.

关键词： Information systems

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Outlook for grid service technologies within the @neurIST ehealth environment

Outlook for grid service technologies within the @neurIST eh...

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作者： Arbona, A. Benkner, S. Fingberg, J. Frangi, A.F. Hofmann, M. Hose, D.R. Lonsdale, G. Ruefenacht, D. Viceconti, M. Grid Systems S.A. Palma de Mallorca Spain Institute of Scientific Computing University of Vienna Austria CandC Research Laboratories NEC Europe Ltd. D-53757 St. Augustin Germany Computational Imaging Lab. Dept. of Technology Pompeu Fabra University Barcelona Spain St. Augustin Germany Dept. Medical Physics and Clinical Engineering U. Sheffield United Kingdom Neuroradiology - HUG University Hospital of Geneva Switzerland Bio Computing Competence Centre Supercomputing Solution S.r.l. Milan Italy

The aim of the @neurIST project is to create an IT infrastructure for the management of all processes linked to research, diagnosis and treatment development for complex and multi-factorial diseases. The IT infrastructure will be developed for one such disease, cerebral aneurysm and subarachnoid haemorrhage, but its core technologies will be transferable to meet the needs of other medical areas. Since the IT infrastructure for @neurIST will need to encompass data repositories, computational analysis services and information systems handling multi-scale, multi-modal information at distributed sites, the natural basis for the IT infrastructure is a grid service middleware. The project will adopt a service-oriented architecture because it aims to provide a system addressing the needs of medical researchers, clinicians and health care specialists (and their IT providers/systems) and medical supplier/consulting industries. © 2006 The authors. All rights reserved.

关键词： Diagnosis

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Accelerating GPGPU architecture simulation 13

Accelerating GPGPU architecture simulation

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Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems

作者： Zhibin Yu Lieven Eeckhout Nilanjan Goswami Tao Li Lizy John Hai Jin Chengzhong Xu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China ELIS Department Ghent University Ghent Belgium Intelligent Design of Efficient Architectures Lab University of Florida Gainesville USA Department of Electrical and Computer Engineering University of Texas at Austin Austin USA Service Computing Technologies and System Lab/Cluster and Grid Computing Lab HUST Wuhan China Department of Electrical and Computer Engineering Wayne State University Detroit USA

ISBN: (纸本)9781450319003

Recently, graphics processing units (GPUs) have opened up new opportunities for speeding up general-purpose parallel applications due to their massive computational power and up to hundreds of thousands of threads enabled by programming models such as CUDA. However, due to the serial nature of existing micro-architecture simulators, these massively parallel architectures and workloads need to be simulated sequentially. As a result, simulating GPGPU architectures with typical benchmarks and input data sets is extremely time-consuming. This paper addresses the GPGPU architecture simulation challenge by generating miniature, yet representative GPGPU kernels. We first summarize the static characteristics of an existing GPGPU kernel in a profile, and analyze its dynamic behavior using the novel concept of the divergence flow statistics graph (DFSG). We subsequently use a GPGPU kernel synthesizing framework to generate a miniature proxy of the original kernel, which can reduce simulation time significantly. The key idea is to reduce the number of simulated instructions by decreasing per-thread iteration counts of loops. Our experimental results show that our approach can accelerate GPGPU architecture simulation by a factor of 88X on average and up to 589X with an average IPC relative error of 5.6%.

关键词： micro-architecture simulation performance general purpose graphics processing unit (GPGPU)

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Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

arXiv

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arXiv 2023年

作者： Qian, Junkai Jiang, Yuning Liu, Xin Wang, Qiong Wang, Ting Shi, Yuanming Chen, Wei The MoE Engineering Research Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University China The Automatic Control Laboratory EPFL Switzerland The School of Information Science and Technology ShanghaiTech University China The State Grid Beijing Electric Power Company China The Department of Electronic Engineering Tsinghua University China

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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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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EuCAPT White paper opportunities and challenges for theoretical astroparticle physics in the next decade

arXiv

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arXiv 2021年

作者： Batista, R. Alves Amin, M.A. Barenboim, G. Bartolo, N. Baumann, D. Bauswein, A. Bellini, E. Benisty, D. Bertone, Gianfranco Blasi, P. Böhmer, C.G. Bošnjak, Ž. Bringmann, T. Burrage, C. Bustamante, M. Bustillo, J. Calderón Byrnes, C.T. Calore, F. Catena, R. Cerdeño, D.G. Cerri, S.S. Chianese, M. Clough, K. Cole, A. Coloma, P. Coogan, A. Covi, L. Cutting, D. Davis, A.C. de Rham, C. di Matteo, A. Domènech, G. Drewes, M. Dietrich, T. Edwards, T.D.P. Esteban, I. Erdem, R. Evoli, C. Fasiello, M. Feeney, S.M. Ferreira, R.Z. Fialkov, A. Fornengo, N. Gabici, S. Galatyuk, T. Gaggero, D. Grasso, D. Guépin, C. Harz, J. Herrero-Valea, M. Hinderer, T. Hogg, N.B. Hooper, D.C. Iocco, F. Isern, J. Karchev, K. Kavanagh, B.J. Korsmeier, M. Kotera, K. Koyama, K. Krishnan, B. Lesgourgues, J. Said, J. Levi Lombriser, L. Lorenz, C.S. Manconi, S. Mapelli, M. Marcowith, A. Markoff, S.B. Marsh, D.J.E. Martinelli, M. Martins, C.J.A.P. Matthews, J.H. Meli, A. Mena, O. Mifsud, J. Bertolami, M.M. Miller Millington, P. Moesta, P. Nippel, K. Niro, V. O’Connor, E. Oikonomou, F. Paganini, C.F. Pagliaroli, G. Pani, P. Pfrommer, C. Pascoli, S. Pinol, L. Pizzuti, L. Porto, R.A. Pound, A. Quevedo, F. Raffelt, G.G. Raccanelli, A. Ramirez-Ruiz, E. Raveri, M. Renaux-Petel, S. Ricciardone, A. Khalifeh, A. Rida Riotto, A. Roiban, R. Rubio, J. Sahlén, M. Sabti, N. Sagunski, L. Šarčević, N. Schmitz, K. Schwaller, P. Schwetz, T. Sedrakian, A. Sellentin, E. Serenelli, A. Serpico, P.D. Sfakianakis, E.I. Shalgar, S. Silvestri, A. Tamborra, I. Tanidis, K. Teresi, D. Tokareva, A.A. Tolos, L. Trojanowski, S. Trotta, R. Uhlemann, C. Urban, F.R. Vernizzi, F. van Vliet, A. Villante, F.L. Vincent, A. Vink, J. Vitagliano, E. Weniger, C. Wickenbrock, Arne Winter, W. Zell, S. Zeng, M. Instituto de Física Teórica UAM/CSIC U. Autónoma de Madrid Cantoblanco C/ Nicolás Cabrera 13-15 Madrid28049 Spain Physics and Astronomy Department Rice University 6100 Main MS-61 HoustonTX77005-1827 United States Departament de Física Teòrica IFIC Universitat de València CSIC BurjassotE-46100 Spain Dipartimento di Fisica e Astronomia Galileo Galilei Università degli Studi di Padova via F. Marzolo PadovaI-35131 Italy Institute for Theoretical Physics Amsterdam Delta Institute for Theoretical Physics University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands GSI Helmholtzzentrum für Schwerionenforschung DarmstadtD-64291 Germany GSI Helmholtz Center for Heavy Ion Research Campus Darmstadt Germany Dèpartement de Physique Thèorique Universitè de Genève 24 quai Ernest Ansermet Genève 41211 Switzerland DAMTP Centre for Mathematical Sciences University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Gran Sasso Science Institute Via Michele Iacobucci 2 L’Aquila67100 Italy INFN Laboratori Nazionali del Gran Sasso AQ Assergi67100 Italy Department of Mathematics University College London Gower Street LondonWC1E 6BT United Kingdom Faculty of Electrical Engineering and Computing University of Zagreb Unska ul. 3 Zagreb10000 Croatia Department of Physics University of Oslo Box 1048 OsloN-0316 Norway School of Physics and Astronomy University of Nottingham University Park NottinghamNG7 2RD United Kingdom Niels Bohr Institute University of Copenhagen Blegdamsvej 17 Copenhagen2100 Denmark Instituto Galego de Física de Altas Enerxías Universidade de Santiago de Compostela Santiago de CompostelaE-15782 Spain Department of Physics and Astronomy University of Sussex BrightonBN1 9QH United Kingdom Univ. Grenoble Alpes USMB CNRS LAPTh AnnecyF-74000 France Chalmers University of Technology Department of Physics GöteborgSE-412 96 Sweden Dep

Astroparticle physics is undergoing a profound transformation, due to a series of extraordinary new results, such as the discovery of high-energy cosmic neutrinos with IceCube, the direct detection of gravitational waves with LIGO and Virgo, and many others. This white paper is the result of a collab.rative effort that involved hundreds of theoretical astroparticle physicists and cosmologists, under the coordination of the European Consortium for Astroparticle Theory (EuCAPT). Addressed to the whole astroparticle physics community, it explores upcoming theoretical opportunities and challenges for our field of research, with particular emphasis on the possible synergies among different subfields, and the prospects for solving the most fundamental open questions with multi-messenger observations. © 2021, CC BY-NC-ND.

关键词： Cosmology

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