检索结果-内蒙古大学图书馆

Conference on High-Performance Computing in Geoscience and Remote Sensing VI

作者： Lazcano, R. Sidrach-Cardona, I. Madronal, D. Desnos, K. Pelcat, M. Juarez, E. Sanz, C. Tech Univ Madrid UPM Ctr Software Technol & Multimedia Syst CITSEM Madrid Spain UEB INSA Rennes IETR CNRS UMR 6164 Rennes France

ISBN: (纸本)9781510604186;9781510604193

Hyperspectral imaging (HI) collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. The tremendous development of this technology within the field of remote sensing has led to new research fields, such as cancer automatic detection or precision agriculture, but has also increased the performance requirements of the applications. For instance, strong time constraints need to be respected, since many applications imply real-time responses. Achieving real-time is a challenge, as hyperspectral sensors generate high volumes of data to process. Thus, so as to achieve this requisite, first the initial image data needs to be reduced by discarding redundancies and keeping only useful information. Then, the intrinsic parallelism in a system specification must be explicitly highlighted. In this paper, the PCA (Principal Component Analysis) algorithm is implemented using the RVC-CAL dataflow language, which specifies a system as a set of blocks or actors and allows its parallelization by scheduling the blocks over different processing units. Two implementations of PCA for hyperspectral images have been compared when aiming at obtaining the first few principal components: first, the algorithm has been implemented using the Jacobi approach for obtaining the eigenvectors;thereafter, the NIPALS-PCA algorithm, which approximates the principal components iteratively, has also been studied. Both implementations have been compared in terms of accuracy and computation time;then, the parallelization of both models has also been analyzed. These comparisons show promising results in terms of computation time and parallelization: the performance of the NIPALS- PCA algorithm is clearly better when only the first principal component is achieved, while the partitioning of the algorithm execution over several cores shows an important speedup for the PCA-Jacobi. Thus, experimental results show the potential of RVC-CAL to automatically generate implemen

关键词： Hyperspectral Imaging Principal Component Analysis RVC-CAL Real-time processing parallelism exploitation

来源：评论

学校读者我要写书评

暂无评论

exploitation of parallelism to nested loops with dependence cycles

引用

JOURNAL OF SYSTEMS ARCHITECTURE 2004年第12期50卷 729-742页

作者： Chang, WL Chu, CP Ho, M So Taiwan Univ Technol Dept Informat Management Tainan 710 Taiwan Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan

In this paper, we analyze the recurrences from the breakability of the dependence links formed in general multi-statements in a nested loop. The major findings include: (1) A sink variable renaming technique, which can reposition an undesired anti-dependence and/or output-dependence link, is capable of breaking an anti-dependence and/or output-dependence link. (2) For recurrences connected by only true dependences, a dynamic dependence concept and the derived technique are powerful in terms of parallelism exploitation. (3) By the employment of global dependence testing, link-breaking strategy, Tarjan's depth-first search algorithm, and a topological sorting, an algorithm for resolving a general multi-statement recurrence in a nested loop is proposed. Experiments with benchmark cited from Vector loops showed that among 134 subroutines tested, 3 had their parallelism exploitation amended by our proposed method. That is, our offered algorithm increased the rate of parallelism exploitation of Vector loops by approximately 2.24%. (C) 2004 Published by Elsevier B.V.

关键词： parallelizing compilers vectorizing compilers loop optimization data dependence analysis dependence cycle parallelism exploitation

来源：评论

学校读者我要写书评

暂无评论

Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL 7

Parallel exploitation of a spatial-spectral classification a...

引用

Conference on High-Performance Computing in Geoscience and Remote Sensing VII

作者： Lazcano, R. Madronal, D. Fabelo, H. Ortega, S. Salvador, R. Callico, G. M. Juarez, E. Sanz, C. Univ Politecn Madrid Ctr Software Technol & Multimedia Syst CITSEM Madrid Spain ULPGC Res Inst Appl Microelect IUMA Las Palmas Gran Canaria Spain

ISBN: (纸本)9781510613256;9781510613249

Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVC-CAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

关键词： Hyperspectral Imaging Spatial-spectral classification RVC-CAL Real-time processing parallelism exploitation

来源：评论

学校读者我要写书评

暂无评论

EXPLOITING parallelism IN NEURAL NETWORKS ON A DYNAMIC DATA-DRIVEN SYSTEM

引用

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1993年第10期E76A卷 1804-1811页

作者： ALHAJ, AM TERADA, H Osaka Univ Suita Japan

High speed simulation of neural networks can be achieved through parallel implementations capable of exploiting their massive inherent parallelism. In this paper, we show how this inherent parallelism can be effectively exploited on parallel data-driven systems. By using these systems, the asynchronous parallelism of neural networks can be naturally specified by the functional data-driven programs, and maximally exploited by the pipelined and scalable data-driven processors. We shall demonstrate the suitability of data-driven systems for the parallel simulation of neural networks through a parallel implementation of the widely used back propagation networks. The implementation is based on the exploitation of the network and training set parallelisms inherent in these networks, and is evaluated using an image data compression network.

关键词： DATA-DRIVEN SYSTEMS NEURAL NETWORKS BACK PROPAGATION parallelism exploitation PARALLEL SIMULATION

来源：评论

学校读者我要写书评

暂无评论

Parallel implementation of a hyperspectral image linear SVM classifier using RVC-CAL 6

Parallel implementation of a hyperspectral image linear SVM ...

引用

Conference on High-Performance Computing in Geoscience and Remote Sensing VI

作者： Madronal, D. Fabelo, H. Lazcano, R. Callico, G. M. Juarez, E. Sanz, C. Tech Univ Madrid UPM Ctr Software Technol & Multimedia Syst CITSEM Madrid Spain Univ Las Palmas de Gran Canaria ULPGC Res Inst Appl Microelect IUMA Las Palmas Gran Canaria Las Palmas Spain

ISBN: (纸本)9781510604186;9781510604193

Hyperspectral Imaging (HI) collects high resolution spectral information consisting of hundreds of bands across the electromagnetic spectrum -from the ultraviolet to the infrared range-. Thanks to this huge amount of information, an identification of the different elements that compound the hyperspectral image is feasible. Initially, HI was developed for remote sensing applications and, nowadays, its use has been spread to research fields such as security and medicine. In all of them, new applications that demand the specific requirement of real-time processing have appear. In order to fulfill this requirement, the intrinsic parallelism of the algorithms needs to be explicitly exploited. In this paper, a Support Vector Machine (SVM) classifier with a linear kernel has been implemented using a dataflow language called RVC-CAL. Specifically, RVC-CAL allows the scheduling of functional actors onto the target platform cores. Once the parallelism of the classifier has been extracted, a comparison of the SVM classifier implementation using LibSVM -a specific library for SVM applications-and RVC-CAL has been performed. The speedup results obtained for the image classifier depends on the number of blocks in which the image is divided;concretely, when 3 image blocks are processed in parallel, an average speed up above 2.50, with regard to the RVC-CAL sequential version, is achieved.

关键词： Hyperspectral Imaging Support Vector Machine RVC-CAL Real-time processing parallelism exploitation

来源：评论

学校读者我要写书评

暂无评论

INFRASTRUCTURE-AGNOSTIC PROGRAMMING AND INTEROPERABLE EXECUTION IN HETEROGENEOUS GRIDS

引用

COMPUTING AND INFORMATICS 2016年第4期35卷 986-1004页

作者： Tejedor, Enric Alvarez, Javier Badia, Rosa M. CERN Meyrin Switzerland Univ Adelaide Adelaide SA Australia Barcelona Supercomp Ctr BSC CNS Jordi Girona 29 Barcelona 08034 Spain Spanish Council Sci Res CSIC Artificial Intelligence Res Inst IIIA E-08193 Barcelona Spain

In distributed environments, no matter the type of infrastructure (cluster, grid, cloud), portability of applications and interoperability are always a major concern. Such infrastructures have a high variety of characteristics, which brings a need for systems that abstract the application from the particular details of each infrastructure. In addition, managing parallelisation and distribution also complicates the work of the programmer. In that sense, this paper demonstrates how an e-Science application can be easily developed with the COMPSs programming model and then parallelised in heterogeneous grids with the COMPSs runtime. With COMPSs, programs are developed in a totally-sequential way, while the user is only responsible for specifying their tasks, i.e. computations to be spawned asynchronously to the available resources. The COMPSs runtime deals with parallelisation and infrastructure management, so that the application is portable and agnostic of the underlying infrastructure.

关键词： Grid programming models workflow managers parallelism exploitation

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：