检索结果-内蒙古大学图书馆

Scalable and Robust Demand Response With Mixed-Integer Constraints

IEEE TRANSACTIONS ON SMART GRID 2013年第4期4卷 2089-2099页

作者： Kim, Seung-Jun Giannakis, Georgios B. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

A demand response (DR) problem is considered entailing a set of devices/subscribers, whose operating conditions are modeled using mixed-integer constraints. Device operational periods and power consumption levels are optimized in response to dynamic pricing information to balance user satisfaction and energy cost. Renewable energy resources and energy storage systems are also incorporated. Since DR becomes more effective as the number of participants grows, scalability is ensured through a parallel distributed algorithm, in which a DR coordinator and DR subscribers solve individual subproblems, guided by certain coordination signals. As the problem scales, the recovered solution becomes near-optimal. Robustness to random variations in electricity price and renewable generation is effected through robust optimization techniques. Real-time extension is also discussed. Numerical tests validate the proposed approach.

关键词： Lagrange relaxation mixed-integer programs parallel and distributed algorithms real-time demand response robust optimization

来源：评论

学校读者我要写书评

暂无评论

parallel and distributed Frequent-Regular pattern mining using vertical format in large databases

Parallel and Distributed Frequent-Regular pattern mining usi...

引用

Fourth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom2012)

作者： G. Vijay Kumar V. Valli Kumari School of Computing K L University Guntur India Department of CS&SE AU College of Engineering Visakhapatnam India

ISBN: (纸本)9781634396226

A good number of parallel and distributed frequent pattern mining algorithms have been proposed so far for the large and/or distributed databases. Not only occurrence frequency of a pattern but also occurrence behavior (regularity) of a pattern may be treated as an emerging area in data mining research. So far some efforts have been made to mine regular patterns but there is no suitable algorithm exists to mine frequent-regular patterns in parallel and distributed environment. Therefore, in this paper we introduced a new method called PFRP-method (parallel Frequent Regular Pattern-method) to discover frequent-regular patterns in large databases using vertical data format which requires only one database scan. Our method works in parallel at each local site in order to reduce I/O cost and inter-process communication, generates all frequent-regular patterns in the final phase. Our experiment results show that our PFRP-method is highly efficient in large databases.

关键词： Frequent patterns Regular patterns Large databases Vertical data format parallel and distributed algorithms parallel Lines Database large database LARGE gene indium MINE data formats

来源：评论

学校读者我要写书评

暂无评论

GPU Strategies for Distance-Based Outlier Detection

引用

IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2016年第11期27卷 3256-3268页

作者： Angiulli, Fabrizio Basta, Stefano Lodi, Stefano Sartori, Claudio Univ Calabria DIMES Dept Via P Bucci41C I-87036 Arcavacata Di Rende CS Italy Italian Natl Res Council Inst High Performance Comp & Networking Via P Bucci41C I-87036 Arcavacata Di Rende CS Italy Univ Bologna Dept Comp Sci & Engn Viale Risorgimento 2 I-40136 Bologna Italy

The process of discovering interesting patterns in large, possibly huge, data sets is referred to as data mining, and can be performed in several flavours, known as "data mining functions." Among these functions, outlier detection discovers observations which deviate substantially from the rest of the data, and has many important practical applications. Outlier detection in very large data sets is however computationally very demanding and currently requires high-performance computing facilities. We propose a family of parallel and distributed algorithms for graphic processing units (GPU) derived from two distance-based outlier detection algorithms: BruteForce and SolvingSet. The algorithms differ in the way they exploit the architecture and memory hierarchy of the GPU and guarantee significant improvements with respect to the CPU versions, both in terms of scalability and exploitation of parallelism. We provide a detailed discussion of their computational properties and measure performances with an extensive experimentation, comparing the several implementations and showing significant speedups.

关键词： Distance-based outliers outlier detection parallel and distributed algorithms

来源：评论

学校读者我要写书评

暂无评论

distributed Matrix Completion and Robust Factorization

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2015年第1期16卷 913-960页

作者： Mackey, Lester Talwalkar, Ameet Jordan, Michael I. Stanford Univ Dept Stat 390 Serra Mall Stanford CA 94305 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA

If learning methods are to scale to the massive sizes of modern data sets, it is essential for the field of machine learning to embrace parallel and distributed computing. Inspired by the recent development of matrix factorization methods with rich theory but poor computational complexity and by the relative ease of mapping matrices onto distributed architectures, we introduce a scalable divide-and-conquer framework for noisy matrix factorization. We present a thorough theoretical analysis of this framework in which we characterize the statistical errors introduced by the "divide" step and control their magnitude in the "conquer" step, so that the overall algorithm enjoys high-probability estimation guarantees comparable to those of its base algorithm. We also present experiments in collaborative filtering and video background modeling that demonstrate the near-linear to superlinear speed-ups attainable with this approach.

关键词： collaborative filtering divide-and-conquer matrix completion matrix factorization parallel and distributed algorithms randomized algorithms robust matrix factorization video surveillance

来源：评论

学校读者我要写书评

暂无评论

parallel newton two-stage multisplitting iterative methods for nonlinear systems

引用

BIT NUMERICAL MATHEMATICS 2003年第5期43卷 849-861页

作者： Arnal, J Migallón, V Penadés, J Univ Alicante Dept Ciencia Computac & Inteligencia Artificial E-03071 Alicante Spain

parallel Newton two-stage iterative methods to solve nonlinear systems are studied. These algorithms are based on both the multisplitting technique and the two-stage iterative methods. Convergence properties of these methods are studied when the Jacobian matrix is either monotone or an H-matrix. Furthermore, in order to illustrate the performance of the algorithms studied, computational results about these methods on a distributed memory multiprocessor are discussed.

关键词： parallel and distributed algorithms nonlinear systems numerical methods multisplitting methods two-stage methods distributed memory

来源：评论

学校读者我要写书评

暂无评论

Special Issue: Bio-Inspired Optimization Techniques for High Performance Computing Preface

引用

NEW GENERATION COMPUTING 2011年第2期29卷 125-128页

作者： Folino, Gianluigi Mastroianni, Carlo ICAR CNR I-87036 Arcavacata Di Rende CS Italy

An introduction is presented in which the editor discusses various reports within the issue on topics including the distributed techniques for optimization problems, the use of ant-based algorithm for mapping Virtual ... 详细信息

关键词： Bio-inspired algorithms Optimization Swarm Intelligence Ant Colony Optimization parallel and distributed algorithms

来源：评论

学校读者我要写书评

暂无评论

RANDOMIZATION IN parallel algorithms

引用

STATISTICAL SCIENCE 1993年第1期8卷 65-69页

作者： RAMACHANDRAN, V UNIV TEXAS DEPT COMP SCIAUSTINTX 78712

A randomized algorithm is one that uses random numbers or bits during the runtime of the algorithm. Such algorithms, when properly designed, can ensure a correct solution on every input with high probability. For many problems, randomized algorithms have been designed that are simpler or more efficient than the best deterministic algorithms known for the problems. In this article, we define a natural randomized parallel complexity class, RNC, and give a survey of randomized algorithms for problems in this class.

关键词： ANALYSIS OF algorithms AND PROBLEM COMPLEXITY COMBINATORIAL PROBABILITY NONNUMERIC algorithms parallel and distributed algorithms parallel COMPUTATION

来源：评论

学校读者我要写书评

暂无评论

distributed nearest neighbor-based condensation of very large data sets

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2007年第12期19卷 1593-1606页

作者： Angiulli, Fabrizio Folino, Gianluigi Univ Calabria Dipartimento Elettr Informat & Sistemist I-87036 Arcavacata Di Rende CS Italy Italian Natl Res Council Inst High Performance Comp & Networking I-87036 Arcavacata Di Rende CS Italy

In this work, the parallel Fast Condensed Nearest Neighbor (PFCNN) rule, a distributed method for computing a consistent subset of a very large data set for the nearest neighbor classification rule is presented. In order to cope with the communication overhead typical of distributed environments and to reduce memory requirements, different variants of the basic PFCNN method are introduced. An analysis of spatial cost, CPU cost, and communication overhead is accomplished for all the algorithms. Experimental results, performed on both synthetic and real very large data sets, revealed that these methods can be profitably applied to enormous collections of data. Indeed, they scale up well and are efficient in memory consumption, confirming the theoretical analysis, and achieve noticeable data reduction and good classification accuracy. To the best of our knowledge, this is the first distributed algorithm for computing a training set consistent subset for the nearest neighbor rule.

关键词： classification parallel and distributed algorithms nearest neighbor rule data condensation

来源：评论

学校读者我要写书评

暂无评论

An upper diagonal work distribution for LU decomposition on the HP SuperDome

An upper diagonal work distribution for LU decomposition on ...

引用

3rd International Conference on Communications in Computing

作者： Steck, T Meyer, GGL Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)1932415068

A new family of parameterized LU decomposition algorithms, JHU LU, is presented. For moderate size matrices, JHU LU is twice as fast as vendor-tuned ScaLAPACK pdgetrf In contrast to algorithms inspired by geometrically regular data distribution, JHU L U is based on an upper diagonal work distribution. Comparatively coarse-grained parallelism is used to reduce the total number of communications between processors while exploiting high per-processor performance available by using BLAS subroutines. Another key departure from ScaLAPACK and other libraries is a requirement that the algorithms finish with the resulting factors L and U residing in a single processor memory - in particular, the processor memory that instially stores the matrix A to be decomposed. During processing, JHU L U iteratively updates the data distribution with processors dropping off successively, much like a pipeline discharge. This allows computation to continue while gathering results. Algorithm parameters control the number of processors used, problem size, and block size. A new set of parameters controls the quantity of work done by each processor. JHU L U is implemented on an HP SuperDome system with 550 MHz processors and is compared to ScaLAPACK pdgetrf Using 16 processors, the time to factor a 1000 x 1000 matrix is 195 ms for JHU and 390 ms for ScaLAPACK. In addition, for matrices between 500 x 500 and 3000 x 3000, JHU L U achieves faster execution.

关键词： LU Gaussian elimination parallel and distributed algorithms high performance computing block cyclic decomposition

来源：评论

学校读者我要写书评

暂无评论

Techniques for Graph Analytics on Big Data

Techniques for Graph Analytics on Big Data

引用

IEEE International Congress on Big Data

作者： Nisar, M. Usman Fard, Arash Miller, John A. Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (纸本)9780769550060

Graphs enjoy profound importance because of their versatility and expressivity. They can be effectively used to represent social networks, web search engines and genome sequencing. The field of graph pattern matching has been of significant importance and has wide-spread applications. Conceptually, we want to find subgraphs that match a pattern in a given graph. Much work has been done in this field with solutions like Subgraph Isomorphism and Regular Expression matching. With Big Data, scientists are frequently running into massive graphs that have amplified the challenge that this area poses. We study the speedup and communication behavior of three distributed algorithms for inexact graph pattern matching. We also study the impact of different graph partitionings on runtime and network I/O. Our extensive results show that the algorithms exhibit excellent scalable behavior and min-cut partitioning can lead to improved performance under some circumstances, and can drastically reduce the network traffic as well.

关键词： graph analytics big data graph simulation parallel and distributed algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：