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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1

BMC Neurosci 2017年第SUPPL 1期18卷 58-58页

作者： Sue Denham Panayiota Poirazi Erik De Schutter Karl Friston Ho Ka Chan Thomas Nowotny Dongqi Han Sungho Hong Sophie Rosay Tanja Wernle Alessandro Treves Sarah Goethals Romain Brette Tomas Van Pottelbergh Rodolphe Sepulchre Alex D. Bird Hermann Cuntz Pedro J. Gonçalves Jan-Matthis Lueckmann Giacomo Bassetto Marcel Nonnenmacher Jakob H. Macke Audrey J. Sederberg Jason N. MacLean Stephanie E. Palmer Ulisse Ferrari Christophe Gardella Olivier Marre Thierry Mora Emina Ibrahimovic Martin Müller Jean-Pascal Pfister Tushar Chauhan Timothée Masquelier Alexandre Montlibert Benoit R. Cottereau Moritz Helias Jannis Schuecker David Dahmen Sven Goedeke Alexandre Hyafil Ainhoa Hermoso-Mendizabal Pavel E. Rueda-Orozco Santiago Jaramillo David Robbe Jaime de la Rocha Marie Rooy Fani Koukouli David DiGregorio Uwe Maskos Boris Gutkin Andrey Yu Verisokin Darya V. Verveyko Dmitry E. Postnov Willy Wong Omid Talakoub Robert Chen Milos Popovic Dmitriy Lisitsyn Eric Drebitz Iris Grothe Sunita Mandon Andreas Kreiter Udo Ernst Peter A. Robinson Xuelong Zhao Kevin M. Aquino John D. Griffiths Grishma Mehta-Pandejee Natasha Gabay James MacLaurin Somwrita Sarkar Tim Kunze Jens Haueisen Thomas R. Knösche Subutai Ahmad Yuwei Cui Marcus Lewis Jeff Hawkins Simona Olmi Spase Petkoski Fabrice Bartolomei Maxime Guye Viktor Jirsa Hazem Toutounji Daniel Durstewitz Matteo Cantarelli Adrian Quintana Boris Marin Matt Earnshaw Padraig Gleeson Robert Court Robert McDougal R. Angus Silver Salvador Dura-Bernal Stephen Larson William W. Lytton Giovanni Idili Lorenzo Posani Simona Cocco Karel Ježek Rémi Monasson School of Psychology Faculty of Health & Human Sciences Plymouth University Plymouth Devon PL4 8AA UK Institute of Molecular Biology and Biotechnology (IMBB) Foundation for Research and Technology-Hellas (FORTH) Heraklion Crete Greece Okinawa Institute of Science and Technology Graduate University Okinawa Japan Wellcome Trust Centre for Neuroimaging Institute of Neurology UCL London WC1N 3BG UK School of Engineering and Informatics University of Sussex Brighton BN1 9QJ UK Graduate School Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Computational Neuroscience Unit Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Cognitive Neuroscience Area Scuola Internazionale Superiore di Studi Avanzati Trieste 34136 Italy Kavli Institute for Systems Neuroscience NTNU Trondheim 7030 Norway Institut de la Vision Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Paris 75012 France Department of Engineering University of Cambridge Cambridge Cambridgeshire CB2 1PZ UK Ernst Strüngmann Institute for Neuroscience Max Planck Society Frankfurt Germany Frankfurt Institute for Advanced Studies Frankfurt Germany Neural Systems Analysis Research Center Caesar An Associate of the Max Planck Society Bonn Germany Max Planck Institute for Biological Cybernetics Tübingen Germany Bernstein Center for Computational Neuroscience Tübingen Germany Present Address: Biomedical Engineering Department Georgia Institute of Technology Atlanta GA 30306 USA Department of Neurobiology University of Chicago Chicago IL 60637 USA Department of Organismal Biology and Anatomy University of Chicago Chicago IL 60637 USA Committee on Computational Neuroscience University of Chicago Chicago IL 60637 USA Institut de la Vision Sorbonne Universités INSERM Paris France Laboratoire de Physique Statistique Ecole Normale Supérieure Paris France Institute of Molecular Life Sciences University of Zürich 8057 Zürich Switzerland Institute of Neuroinformatics Univer

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A comparison of multi-objective algorithms for the automatic design space exploration of a superscalar system

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2013年 187 AISC卷 489-502页

作者： Calborean, Horia Jahr, Ralf Ungerer, Theo Vintan, Lucian Advanced Computer Architecture and Processing Systems Research Lab Lucian Blaga University of Sibiu E. Cioran Str. No. 4 Sibiu 550025 Romania Institute of Computer Science University of Augsburg Universitaetsstr. 6a 86159 Augsburg Germany

ISBN: (纸本)9783642325472

In today's computer architectures the design spaces are huge, thus making it very difficult to find optimal configurations. One way to cope with this problem is to use Automatic Design Space Exploration (ADSE) techniques. We developed the Framework for Automatic Design Space Exploration (FADSE) which is focused on microarchitectural optimizations. This framework includes several state-of-the art heuristic algorithms. In this paper we selected three of them, NSGA-II and SPEA2 as genetic algorithms as well as SMPSO as a particle swarm optimization, and compared their performance. As test case we optimize the parameters of the Grid ALU Processor (GAP) microarchitecture and then GAP together with the post-link code optimizer GAPtimize. An analysis of the simulation results shows a very good performance of all the three algorithms. SMPSO reveals the fastest convergence speed. A clear winner between NSGA-II and SPEA2 cannot be determined.

关键词： Heuristic algorithms

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Iterative methods in large field electron microscope tomography

Iterative methods in large field electron microscope tomogra...

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作者： Wan, Xiaohua Phan, Sébastien Lawrence, Albert Zhang, Fa Han, Renmin Liu, Zhiyong Ellisman, Mark Key Lab of Intelligent Information Processing and Advanced Computing Research Lab Institute of Computing Technology Chinese Academy of Sciences Beijing China National Center for Microscopy and Imaging Research University of California San Diego CA 92093 United States Chinese Academy of Sciences Graduate University Beijing China State Key Lab for Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Electron tomography (ET) is a powerful technology allowing the three-dimensional (3D) imaging of cellular ultrastructure. These structures are reconstructed from a set of micrographs taken at different sample orientations, the final volume being the solution of a general inverse problem. Two different approaches are used in this context: iterative methods and filtered backprojection. Iterative methods are known to provide high-resolution 3D reconstructions for ET under noisy and incomplete data conditions. However, all previous implementations have been restricted to the straight-line projection models. This is not accurate since electron trajectories in electron microscopes do not follow the straight-line optics assumed for X-rays, and biological samples may warp as a result of being exposed to an electron beam. Compensation for curvilinear trajectories, nonlinear electron optics, and sample warping constitutes a major advance in large-field ET and has made possible resolution down to the molecular level in reconstructions of whole cells. At present these advances are limited to filtered backprojection and have been implemented in the software package TxBR. As the next step in this development, we have modified the ASART method in conjunction with a 3D model. By employing alignment based on general curvilinear trajectories we have been able to show that further improvements can be achieved with iterative methods. We also discuss a unified treatment of the alignment and reconstruction problems within the framework of iterative methods, and the relationship between formulas employed in the update step and cross-validation methods. Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.

关键词： Iterative methods

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BigHouse: A simulation infrastructure for data center systems

BigHouse: A simulation infrastructure for data center system...

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2012 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2012

作者： Meisner, David Wu, Junjie Wenisch, Thomas F. Advanced Computer Architecture Lab University of Michigan United States

ISBN: (纸本)9781467311441

Recently, there has been an explosive growth in Internet services, greatly increasing the importance of data center systems. Applications served from the cloud are driving data center growth and quickly overtaking traditional workstations. Although there are a many tools for evaluating components of desktop and server architectures in detail, scalable modeling tools are noticeably missing. We describe BigHouse a simulation infrastructure for data center systems. Instead of simulating servers using detailed microarchitectural models, BigHouse raises the level of abstraction. Using a combination of queuing theory and stochastic modeling, BigHouse can simulate server systems in minutes rather than hours. BigHouse leverages statistical simulation techniques to limit simulation turnaround time to the minimum runtime needed for a desired accuracy. In this paper, we introduce BigHouse, describe its design, and present case studies for how it has already been applied to build and validate models of data center workloads and systems. Furthermore, we describe statistical techniques incorporated into BigHouse to accelerate and parallelize its simulations, and demonstrate its scalability to model large cluster systems while maintaining reasonable simulation time. © 2012 IEEE.

关键词： Stochastic systems

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MemScale: Active low-power modes for main memory

MemScale: Active low-power modes for main memory

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作者： Deng, Qingyuan Meisner, David Ramos, Luiz Wenisch, Thomas F. Bianchini, Ricardo Dept. of Computer Science Rutgers University United States Advanced Computer Architecture Lab University of Michigan United States

Main memory is responsible for a large and increasing fraction of the energy consumed by servers. Prior work has focused on exploiting DRAM low-power states to conserve energy. However, these states require entire DRAM ranks to be idled, which is difficult to achieve even in lightly loaded servers. In this paper, we propose to conserve memory energy while improving its energyproportionality by creating active low-power modes for it. Specifically, we propose MemScale, a scheme wherein we apply dynamic voltage and frequency scaling (DVFS) to the memory controller and dynamic frequency scaling (DFS) to the memory channels and DRAM devices. MemScale is guided by an operating system policy that determines the DVFS/DFS mode of the memory subsystem based on the current need for memory bandwidth, the potential energy savings, and the performance degradation that applications are willing to withstand. Our results demonstrate that Mem- Scale reduces energy consumption significantly compared to modern memory energy management approaches. We conclude that the potential benefits of the MemScale mechanisms and policy more than compensate for their small hardware cost. Copyright © 2011 ACM.

关键词： Energy conservation

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BigHouse: A simulation infrastructure for data center systems

BigHouse: A simulation infrastructure for data center system...

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IEEE International Symposium on Performance Analysis of Systems and Software

作者： David Meisner Junjie Wu Thomas F. Wenisch Advanced Computer Architecture Lab University of Michigan USA

Recently, there has been an explosive growth in Internet services, greatly increasing the importance of data center systems. Applications served from “the cloud” are driving data center growth and quickly overtaking traditional workstations. Although there are a many tools for evaluating components of desktop and server architectures in detail, scalable modeling tools are noticeably missing. We describe BigHouse a simulation infrastructure for data center systems. Instead of simulating servers using detailed microarchitectural models, BigHouse raises the level of abstraction. Using a combination of queuing theory and stochastic modeling, BigHouse can simulate server systems in minutes rather than hours. BigHouse leverages statistical simulation techniques to limit simulation turnaround time to the minimum runtime needed for a desired accuracy. In this paper, we introduce BigHouse, describe its design, and present case studies for how it has already been applied to build and validate models of data center workloads and systems. Furthermore, we describe statistical techniques incorporated into BigHouse to accelerate and parallelize its simulations, and demonstrate its scalability to model large cluster systems while maintaining reasonable simulation time.

关键词： Servers Calibration Object oriented modeling Analytical models Data models Load modeling

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Does low-power design imply energy efficiency for data centers?

Does low-power design imply energy efficiency for data cente...

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IEEE/ACM International Symposium on Low-Power Electronics and Design

作者： Meisner, David Wenisch, Thomas F. Advanced Computer Architecture Lab University of Michigan United States

ISBN: (纸本)9781612846590

Data center efficiency has quickly become a first-class design goal. In response, many studies have emerged from the academic community and industry using low-power design to help improve the energy efficiency of server hardware. Generally, these proposals hold the assumption that low-power design is inherently better for energy efficiency;this preconception stems mostly from great success in the mobile space with building low-power, energy-efficient systems. We observe that unlike mobile devices, constraining a data center server to a low power budget is arbitrary and higher power design choices can be more energy efficient. We analyze the energy efficiency design space of past commercial server designs and find that high-power servers are generally more energy efficient than low-power ones. Furthermore, we evaluate building low- or high-power server clusters and find that the small increase in the cost of cooling high-powered servers is easily outweighed by their greater efficiency. © 2011 IEEE.

关键词： Energy efficiency

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MemScale: Active low-power modes for main memory

MemScale: Active low-power modes for main memory

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Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems

ISBN: (纸本)9781450302661

Main memory is responsible for a large and increasing fraction of the energy consumed by servers. Prior work has focused on exploiting DRAM low-power states to conserve energy. However, these states require entire DRAM ranks to be idled, which is difficult to achieve even in lightly loaded servers. In this paper, we propose to conserve memory energy while improving its energyproportionality by creating active low-power modes for it. Specifically, we propose MemScale, a scheme wherein we apply dynamic voltage and frequency scaling (DVFS) to the memory controller and dynamic frequency scaling (DFS) to the memory channels and DRAM devices. MemScale is guided by an operating system policy that determines the DVFS/DFS mode of the memory subsystem based on the current need for memory bandwidth, the potential energy savings, and the performance degradation that applications are willing to withstand. Our results demonstrate that Mem-Scale reduces energy consumption significantly compared to modern memory energy management approaches. We conclude that the potential benefits of the MemScale mechanisms and policy more than compensate for their small hardware cost. Copyright © 2011 ACM.

关键词： Energy conservation

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Peak power modeling for data center servers with switched-mode power supplies 10

Peak power modeling for data center servers with switched-mo...

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Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design

作者： Meisner, David Wenisch, Thomas F. Advanced Computer Architecture Lab University of Michigan United States

ISBN: (纸本)9781450301466

Accurately modeling server power consumption is critical in designing data center power provisioning infrastructure. However, to date, most research proposals have used average CPU utilization to infer the power consumption of clusters, typically averaging over tens of minutes per observation. We demonstrate that average CPU utilization is not sufficient to predict peak power consumption accurately. By characterizing the relationship between server utilization and power supply behavior, we can more accurately model the actual peak power consumption. Finally, we introduce a new operating system metric that can capture the needed information to design for peak power with low overhead. Copyright 2010 ACM.

关键词： Electric power utilization

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An automatic design space exploration framework for multicore architecture optimizations

An automatic design space exploration framework for multicor...

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作者： Calborean, Horia Vinţan, Lucian Advanced Computer Architecture and Processing Systems Research Lab. Lucian Blaga University of Sibiu Romania

ISBN: (纸本)9781424473359

During the last years, especially due to the computing systems complexity growth, the need for tools which perform automatic design space exploration becomes more and more stringent. This paper presents a new initiated project having as the main aim developing a software tool, called FADSE (Framework for Automatic Design Space Exploration), that comes to meet this need. It is intended to provide out-of-the- box algorithms capable of solving single and multiobjective optimization problems. It focuses on automatic design space exploration for multicore and manycore systems. This tool is intended to be ftexible, to provide easy development and portability.

关键词： Multiobjective optimization

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