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检索条件"主题词=scalable algorithms"

共 95 条记录，以下是61-70 订阅

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Accelerated Search for Non-Negative Greedy Sparse Decomposition via Dimensionality Reduction 8

Accelerated Search for Non-Negative Greedy Sparse Decomposit...

引用

8th Sensor Signal Processing for Defence Conference (SSPD)

作者： Voulgaris, Konstantinos A. Davies, Mike E. Yaghoobi, Mehrdad Univ Edinburgh Inst Digital Commun Edinburgh EH9 3JL Midlothian Scotland

ISBN: (纸本)9781728105031

Non-negative signals form an important class of sparse signals. Many algorithms have already been proposed to recover such non-negative representations, where greedy and convex relaxed algorithms are among the most popular methods. One fast implementation is the FNNOMP algorithm that updates the non-negative coefficients in an iterative manner. Even though FNNOMP is a good approach when working on libraries of small size, the operational time of the algorithm grows significantly when the size of the library is large. This is mainly due to the selection step of the algorithm that relies on matrix vector multiplications. We here introduce the Embedded Nearest Neighbor (E-NN) algorithm which accelerates the search over large datasets while it is guaranteed to find the most correlated atoms. We then replace the selection step of FNNOMP by E-NN. Furthermore we introduce the Update Nearest Neighbor (U-NN) at the look up table of FNNOMP in order to assure the non-negativity criteria of FNNOMP. The results indicate that the proposed methodology can accelerate FNNOMP with a factor 4 on a real dataset of Raman Spectra and with a factor of 22 on a synthetic dataset.

关键词： Matching Pursuit Orthogonal Matching Pursuit Non-negative Sparse Approximations Non-negative Least Square and Spectral Decomposition scalable algorithms Dimensionality Reduction Linear Embedding Raman Spectroscopy

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Automated Dynamic Data Redistribution 31

Automated Dynamic Data Redistribution

引用

31st IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPS)

作者： Marrinan, Thomas Insley, Joseph A. Rizzi, Silvio Tessier, Francois Papka, Michael E. Argonne Natl Lab Lemont IL 60439 USA Northern Illinois Univ De Kalb IL 60115 USA

ISBN: (纸本)9780769561493

High-performance distributed memory applications often load or receive data in a format that differs from what the application uses. One such difference arises from how the application distributes data for parallel processing. Data must be redistributed from how it was laid out by the producer to how the application needs the data to be laid out amongst its processes. In this paper, we present a large-scale distributed memory library, provided to developers in an easily integrated API, for automating data redistribution in MPI enabled applications. We then present the results of two scientific computing use cases to evaluate our library. The first use case highlights how dynamic data redistribution can greatly reduce load time when reading three-dimensional medical imaging data from disk. The second use case highlights how dynamic data redistribution can facilitate in-transit analysis of computational fluid dynamics, which results in smaller data output size and faster time-to-discovery.

关键词： data redistribution distributed memory MPI scalable algorithms scientific applications

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Scaling into ambient intelligence

Scaling into ambient intelligence

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Design, Automation and Test in Europe Conference and Exhibition (DATE 03)

作者： Basten, T Benini, L Chandrakasan, A Lindwer, M Liu, J Min, R Zhao, F Eindhoven Univ Technol NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)0769518702

Envision the situation that high quality information and entertainment is easily accessible to anyone, anywhere, at any time, and on any device. How realistic is this vision? And what does it require from the underlying technology? Ambient Intelligence (AmI) integrates concepts ranging from ubiquitous computing to autonomous and intelligent systems. An AmI environment will be highly dynamic in many aspects. Underlying technology must be very flexible to cope with this dynamism. Scalability of technology is only one crucial aspect. This paper explores scalability from the processing, the communication, and the software perspectives.

关键词： multi-processor systems on chip high-density wireless networks scalable software infrastructure scalable algorithms low power and energy

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Detecting Quilted Web Pages at Scale 12

Detecting Quilted Web Pages at Scale

引用

35th ACM SIGIR Annual International Conference on Research and Development in Information Retrieval

作者： Najork, Marc Microsoft Res 1065 La Ave Mountain View CA 94043 USA

ISBN: (纸本)9781450316583

Web-based advertising and electronic commerce, combined with the key role of search engines in driving visitors to ad-monetized and e-commerce web sites, has given rise to the phenomenon of web spam: web pages that are of little value to visitors, but that are created mainly to mislead search engines into driving traffic to target web sites. A large fraction of spam web pages is automatically generated, and some portion of these pages is generated by stitching together parts (sentences or paragraphs) of other web pages. This paper presents a scalable algorithm for detecting such "quilted" web pages. Previous work by the author and his collaborators introduced a sampling-based algorithm that was capable of detecting some, but by far not all quilted web pages in a collection. By contrast, the algorithm presented in this work identifies all quilted web pages, and it is scalable to very large corpora. We tested the algorithm on the half-billion page English-language subset of the ClueWeb09 collection, and evaluated its effectiveness in detecting web spam by manually inspecting small samples of the detected quilted pages. This manual inspection guided us in iteratively refining the algorithm to be more efficient in detecting real-world spam.

关键词： Web spam detection phrase-level duplication scalable algorithms

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Mobile Access Record Resolution on Large-Scale Identifier-Linkage Graphs 18

Mobile Access Record Resolution on Large-Scale Identifier-Li...

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24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Shen Xin Yang, Hongxia Xian, Weizhao Ester, Martin Bu, Jiajun Wang, Zhongyao Wang, Can Zhejiang Univ Hangzhou Zhejiang Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781450355520

The e-commerce era is witnessing a rapid increase of mobile Internet users. Major e-commerce companies nowadays see billions of mobile accesses every day. Hidden in these records are valuable user behavioral characteristics such as their shopping preferences and browsing patterns. And, to extract these knowledge from the huge dataset, we need to first link records to the corresponding mobile devices. This Mobile Access Records Resolution (MARR) problem is confronted with two major challenges: (1) device identifiers and other attributes in access records might be missing or unreliable;(2) the dataset contains billions of access records from millions of devices. To the best of our knowledge, as a novel challenge industrial problem of mobile Internet, no existing method has been developed to resolve entities using mobile device identifiers in such a massive scale. To address these issues, we propose a SParse Identifier-linkage Graph (SPI-Graph) accompanied with the abundant mobile device profiling data to accurately match mobile access records to devices. Furthermore, two versions (unsupervised and semi-supervised) of Parallel Graph-based Record Resolution (PGRR) algorithm are developed to effectively exploit the advantages of the large-scale server clusters comprising of more than 1,000 computing nodes. We empirically show superior performances of PGRR algorithms in a very challenging and sparse real data set containing 5.28 million nodes and 31.06 million edges from 2.15 billion access records compared to other state-of-the-arts methodologies.

关键词： Mobile access record resolution scalable algorithms Big data Graph algorithms

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Resource-aware complexity scalability for mobile MPEG encoding

Resource-aware complexity scalability for mobile MPEG encodi...

引用

Conference on Visual Communications and Image Processing 2004

作者： Mietens, S de With, PHN Hentschel, C Philips Res Labs NL-5656 AA Eindhoven Netherlands

ISBN: (纸本)0819452114

Complexity scalability attempts to scale the required resources of an algorithm with the chosen quality settings, in order to broaden the application range. In this paper, we present complexity-scalable MPEG encoding of which the core processing modules are modified for scalability. Scalability is basically achieved for the computational complexity by varying the number of computed DCT coefficients and the number of evaluated motion vectors, while other modules are designed such that they scale with the previous parameters. Resource usage such as power-consuming memory accesses scale accordingly. The interdependencies of the scalable modules and the system performance are evaluated. Experimental results show scalability giving a smooth change in complexity and corresponding video quality. The elapsed execution time of the scalable encoder, reflecting the computational complexity, can be gradually reduced to roughly 50% of its execution time when operating at high quality. The video quality scales between 21.5 dB and 38.5 dB PSNR for different sequences targeting 1500 kbps. The implemented encoder and the scalability techniques can be successfully applied in mobile systems based on MPEG video compression, and also has advantages in multi-tasking environments like high-end TV sets. The obtained scalability techniques can be applied to other coding standards like MPEG-4 or H.264 as well.

关键词： MPEG encoding scalable algorithms resource scalability

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Linked Data Partitioning for RDF Processing on Apache Spark 3

Linked Data Partitioning for RDF Processing on Apache Spark

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3rd International Conference on Web Research (ICWR)

作者： Atashkar, Amir Hossein Ghadiri, Nasser Joodaki, Mehdi Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran

ISBN: (纸本)9781538604205

RDF models are widely used in the web of data due to their flexibility and similarity to graph patterns. Because of the growing use of RDFs, their volumes and contents are increasing. Therefore, processing of such massive amount of data on a single machine is not efficient enough, because of the response time and limited hardware resources. A common approach to overcome this limitation is cluster processing and huge datasets could benefit distributed cluster processing on Apache Hadoop. Because of using too much of hard disks, the processing time is usually inadequate. In this paper, we propose a partitiong approach based on Apache Spark for rapid processing of RDF data models. A key feature of Apache Spark is using main memory instead of hard disk, so the speed of data processing in our method is improved. We have evaluated the proposed method by runing SQL queris on RDF data which partitioned on the cluster and demonstrates improved performance.

关键词： Linked data scalable algorithms NoSQL big data

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Efficient Exploration of the Rashomon Set of Rule-Set Models 24

Efficient Exploration of the Rashomon Set of Rule-Set Models

引用

30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Ciaperoni, Martino Xiao, Han Gionis, Aristides Aalto Univ Espoo Finland Upright Project Helsinki Finland KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9798400704901

Today, as increasingly complex predictive models are developed, simple rule sets remain a crucial tool to obtain interpretable predictions and drive high-stakes decision making. However, a single rule set provides a partial representation of a learning task. An emerging paradigm in interpretable machine learning aims at exploring the Rashomon set of all models exhibiting near-optimal performance. Existing work on Rashomon-set exploration focuses on exhaustive search of the Rashomon set for particular classes of models, which can be a computationally challenging task. On the other hand, exhaustive enumeration leads to redundancy that often is not necessary, and a representative sample or an estimate of the size of the Rashomon set is sufficient for many applications. In this work, we propose, for the first time, efficient methods to explore the Rashomon set of rule-set models with or without exhaustive search. Extensive experiments demonstrate the effectiveness of the proposed methods in a variety of scenarios.

关键词： Interpretable machine learning Rashomon set Rule-based classification scalable algorithms

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Towards scalable Graph Computation on Mobile Devices 2

Towards Scalable Graph Computation on Mobile Devices

引用

IEEE International Conference on Big Data

作者： Chen, Yiqi Lin, Zhiyuan Pienta, Robert Kahng, Minsuk Chau, Duen Horng Georgia Tech Coll Comp Atlanta GA 30332 USA

ISBN: (纸本)9781479956661

Mobile devices have become increasingly central to our everyday activities, due to their portability, multi-touch capabilities, and ever-improving computational power. Such attractive features have spurred research interest in leveraging mobile devices for computation. We explore a novel approach that aims to use a single mobile device to perform scalable graph computation on large graphs that do not fit in the device's limited main memory, opening up the possibility of performing on-device analysis of large datasets, without relying on the cloud. Based on the familiar memory mapping capability provided by today's mobile operating systems, our approach to scale up computation is powerful and intentionally kept simple to maximize its applicability across the iOS and Android platforms. Our experiments demonstrate that an iPad mini can perform fast computation on large real graphs with as many as 272 million edges (Google+ social graph), at a speed that is only a few times slower than a 13 '' Macbook Pro. Through creating a real world iOS app with this technique, we demonstrate the strong potential application for scalable graph computation on a single mobile device using our approach.

关键词： graph mining scalable algorithms mobile device memory mapping

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A hardware-assisted scalable solution for interactive volume rendering of time-varying data

A hardware-assisted scalable solution for interactive volume...

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7th IEEE Symposium on Information Visualization (INFOVIS 2001)

作者： Lum, EB Ma, KL Clyne, J Univ Calif Davis CIPIC Davis CA 95616 USA Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Natl Ctr Atmospher Res Boulder CO 80303 USA

We present a scalable volume rendering technique that exploits lossy compression and low-cost commodity hardware to permit highly interactive exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3D graphics card. Using a single PC equipped with a modest amount of memory, a texture capable graphics card, and an inexpensive disk array, we are able to render hundreds of time steps of regularly gridded volume data (up to 42 million voxels each time step) at interactive rates. By clustering multiple PCs together, we demonstrate the data-size scalability of our method. The frame rates achieved make possible the interactive exploration of data in the temporal, spatial, and transfer function domains. A comprehensive evaluation of our method based on experimental studies using data sets (up to 134 million voxels per time step) from turbulence flow simulations is also presented.

关键词： compression disk I/O high performance computing out-of-core processing parallel rendering PC scalable algorithms scientific visualization texture hardware time-varying data transform encoding volume rendering

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