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检索条件"任意字段=5th International Conference on Algorithms and Architectures for Parallel Processing"

共 3293 条记录，以下是1171-1180 订阅

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Concur 2003 : Concurrency theory : 14th international conference, Marseille, France, September 3-5, 2003

引用

2016年

作者： roberto informatiker. ger amadio

Concur 2003 : Concurrency theory : 14th international conference, Marseille, France, September 3-5, 2003 by Amadio, Roberto Informatiker. Ger; Amadio, Roberto Informaticien. Fre; Amadio, Roberto Computer Scientist. En... 详细信息

关键词： algorithmes distribués + algorithmes parallèles (méthodes de programmation) concur concurrency concurrent programming + distributed programming + parallel programming (programming methods) conference, distributed algorithms + parallel algorithms (programming methods) france, international parallel processing + concurrency (operating systems) parallelverarbeitung + nebenläufigkeit (betriebssysteme) programmation distribuée + programmation parallèle (méthodes de programmation) traitement parallele + simultanéité (systèmes d'exploitation) verteilte algorithmen + parallele algorithmen (programmiermethoden) verteilte programmierung + parallele programmierung (programmiermethoden)

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A parallel Multi-threaded Solver for Symmetric Positive Definite Bordered-Band Linear Systems 11th

A Parallel Multi-threaded Solver for Symmetric Positive Defi...

引用

11th international conference on parallel processing and Applied Mathematics (PPAM)

作者： Benner, Peter Ezzatti, Pablo Quintana-Orti, Enrique S. Remon, Alfredo Max Planck Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany Univ Republica Inst Comp Montevideo 11300 Uruguay Univ Jaime I Dept Ingn & Ciencia Comp Castellon de La Plana 12701 Spain

ISBN: (纸本)9783319321493;9783319321486

We present a multi-threaded solver for symmetric positive definite linear systems where the coefficient matrix of the problem features a bordered-band non-zero pattern. the algorithms that implement this approach heavily rely on a compact storage format, tailored for this type of matrices, that reduces the memory requirements, produces a regular data access pattern, and allows to cast the bulk of the computations in terms of efficient kernels from the Level-3 and Level-2 BLAS. the efficiency of our approach is illustrated by numerical experiments.

关键词： Bordered-band matrices Symmetric positive definite linear systems Cholesky factorization Multicore architectures

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GPGPU based Concurrent Classification using Trained Model of Handwritten Digits

GPGPU based Concurrent Classification using Trained Model of...

引用

10th international conference on Open Source Systems and Technologies (ICOSST)

作者： Rizvi, Syed Tahir Hussain Cabodi, Gianpiero Arif, Arslan Javed, Muhammad Yaqoob Gulzar, Muhammad Majid Politecn Torino Turin Italy Univ Sci & Technol China Hefei Peoples R China Univ Cent Punjab Lahore Pakistan

ISBN: (纸本)9781509055869

In this paper, General Purpose Graphical processing Unit (GPGPU) based concurrent implementation of handwritten digit classifier is presented. Different styles of handwriting make it difficult to recognize a pattern but using neural network, it is not a difficult task to perform. Different softwares like torch and MATLAB provide the support of multiple training algorithms to train a network. By choosing an appropriate training algorithm for a specific application, speed of training can be increased. Furthermore, using computational power of GPUs, training and classification speed of neural network can be significantly improved. In this work, Modified National Institute of Standards and Technology (MNIST) database of handwritten digits is used to train the network. Accuracy and training time of digit classifier is evaluated for different algorithms and then concurrent training is performed by exploiting power of GPU. Trained parameters are imported and used for the concurrent classification with Compute Unified Device Architecture (CUDA) computing language which can be useful in numerous practical applications. Finally, the results of sequential and concurrent operations of training and classification are compared.

关键词： Concurrent parallel architectures Neural network Pattern recognition MATLAB Image Classification

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A GPU-Based Breast Cancer Detection System Using Single Pass Fuzzy C-Means Clustering Algorithm

A GPU-Based Breast Cancer Detection System Using Single Pass...

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5th international conference on Multimedia Computing and Systems (ICMCS)

作者： Al-Ayyoub, Mahmoud AlZu'bi, Shadi M. Jararweh, Yaser Alsmirat, Mohammad A. Jordan Univ Sci & Technol Irbid Jordan AlZaytoonah Univ Jordan Amman Jordan

ISBN: (纸本)9781509051465

Br east cancer is the most lethal type of cancer affecting women in the world. To improve the life quality of women, an early detection of this malignancy is always promising because this cancer is one of the cancers that can be managed and treated easily if it is detected earlier. Mammography is the standard screening method to diagnose breast cancer. At these days, there are a lot of researches that have been done to improve this screening method by benefiting from the enormous growth of technology. Graphics processing Unit (GPU) is a parallel processor that can divide the complex computations tasks into subtasks and run them concurrently. Many medical imaging modalities offloaded their processing to this processor to help in improving the speed of healthcare systems in order to diagnose the illnesses in real time. this research introduces an acceleration method for the segmentation of the mammography images based on the GPU. In order to provide a better detection for the cancerous tumor, we use a modified version of the most common algorithm for image segmentation, which is the Single Pass Fuzzy C-Means (FCM) algorithm. the approach will be applied to a set of mammogram images to distinguish between malignant and benign cases. Additionally, the system is implemented on GPU parallel processor as well as the traditional CPU in order to compare the performance of both implementations. the performance results are compared according to the execution time and the speedup metrics. the proposed implementation on GPU provides a fine speedup compared to its serial implementation on CPU.

关键词： Image processing GPU Breast Cancer Detection FCM

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VIDEO processing USING GPU-ACCELERATOR UNDER DESKTOP VIRTUALIZATION ENVIRONMENT 5

VIDEO PROCESSING USING GPU-ACCELERATOR UNDER DESKTOP VIRTUAL...

引用

5th international conference on Audio, Language and Image processing (ICALIP)

作者： Liu, Dan Zhang, Huiran Zhou, Jichao Shen, Wenfeng Cao, Min Chen, Shengbo Qian, Quan Dai, Dongbo Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Univ Mat Genome Inst 99 Shangda Rd Shanghai 200444 Peoples R China

ISBN: (纸本)9781509006540

the desktop virtualization environment could propose a solution for the high-definition video processing, which used multi-GPU collaboration and parallel computing. the multi-GPU parallel encoding computing is often implemented by multi-threads mode. Based on the analysis of the GPU multi-level storage structure, and data transmission between CPU and GPU, pinned memory (zero-copy memory) and shared memory approaches are used to shorten the time of encoded video data transmission between devices. In this paper the latest support low-latency remote display's GPU and H.264 compression standard are used for the desktop virtualization environment. the experimental results show that the usage of CPU and the transmission bandwidth has been significantly decreased, and the performance of virtual environments has been improved.

关键词： GPU parallel computing desktop virtualization pinned memory H.264

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parallel Ant Brood Graph Partitioning in Julia 11th

Parallel Ant Brood Graph Partitioning in Julia

引用

11th international conference on parallel processing and Applied Mathematics (PPAM)

作者： Chan, Jose Juan Mijares Mao, Yuyin Liu, Ying Ying thulasiraman, Parimala thulasiram, Ruppa K. Univ Manitoba Winnipeg MB Canada

ISBN: (纸本)9783319321523;9783319321516

Many big data applications are usually categorized as irregular. Irregular problems feature unpredictable and unstructured properties in terms of the program flow, data access pattern and typically use pointer-based data structures such as graphs. the problems are data, compute and communication intensive in nature. the algorithms are therefore designed and implemented on high performance architectures. the first stage of the parallel algorithm design is data partitioning. In this stage, the data is sub-divided into equally sized disjoint elements such that the communication volume among the processors is minimized. If the data is represented as a graph, it can be stated as the graph partitioning problem, which is NP-hard. In this work, we consider the metaheuristic, ant brooding algorithm based on larval sorting by ants to solve the graph partitioning problem. the parallel ant brooding algorithm is implemented on a cluster using MIT's Julia language. We test the parallel algorithm on different benchmark and synthetic graphs. We compare our Julia parallel implementation with Julia sequential and C sequential implementations. We found that the performance of Julia is comparable to C with good scalability, and the parallel Julia implementation achieves speedup greater than 1 for a synthetic graph with 200 vertices and 1000 edges.

关键词： Julia language Ant brooding Graph partitioning

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Comparison of GPU-based and CPU-based algorithms for determining the minimum distance between a CUSA scalper and blood vessels 7

Comparison of GPU-based and CPU-based algorithms for determi...

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7th international conference on Bioinformatics Models, Methods and algorithms, BIOINFORMATICS 2016 - Part of 9th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016

作者： Noborio, Hiroshi Kunii, Takahiro Mizushino, Kiminori Department of Computer Science Osaka Electro-Commun. Univ. Kiyotaki 1130-70 Shijo-Nawate Osaka575-0063 Japan Kashina System Co. Hirata-Cho 116-22 Hikone Shiga522-0041 Japan Embedded Wings Co. Ine 5-2-3 Minoh Osaka562-0015 Japan

ISBN: (纸本)9789897581700

In this study, we have designed a GPGPU (General-Purpose Graphics processing Unit)-based algorithm for determining the minimum distance from the tip of a CUSA (Cavitron Ultrasonic Surgical Aspirator) scalpel to the closest point around three types of blood vessel STLs (STereo-Lithographies). the algorithm consists of the following two functions: First, we use z-buffering (depth buffering) as the classic matured function of the GPU in order to effectively obtain depths corresponding to image pixels. Second, we use multiple cores of the GPU for parallel processing so as to calculate the minimum Euclidean distance from the scalpel tip to the closest z-values of the depths. therefore, the complexity of the GPU-based algorithm does not depend on the shape complexity (e.g., patch, edge, and vertex numbers) of the blood vessels. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Graphics processing unit

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Dataflow IR for memory efficient RIPL compilation to FPGAs 16th

Dataflow IR for memory efficient RIPL compilation to FPGAs

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16th international conference on algorithms and architectures for parallel processing - ICA3PP 2016 Collocated Workshops: 1st Workshop on Supercomputing Co-Design Technology, SCDT-2016, 1st international Workshop on theoretical Approaches to Performance Evaluation, Modeling and Simulation, TAPEMS 2016, 1st international Workshop on Trust, Security and Privacy for Big Data, BigTrust 2016, 1st edition of the workshop on Ultrascale Computing for Early Researchers, UCER 2016 and 1st international Workshop on Data Locality in Modern Computing Systems, DLMCS 2016

作者： Stewart, Robert Michaelson, Greg Bhowmik, Deepayan Garcia, Paulo Wallace, Andy School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh United Kingdom School of Engineering and Physical Sciences Heriot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9783319499550

Field programmable gate arrays (FPGAs) are fundamentally different to fixed processors architectures because their memory hierarchies can be tailored to the needs of an algorithm. FPGA compilers for high level languages are not hindered by fixed memory hierarchies. the constraint when compiling to FPGAs is the availability of resources. In this paper we describe how the dataflow intermediary of our declarative FPGA image processing DSL called RIPL (Rathlin Image processing Language) enables us to constrain memory. We use five benchmarks to demonstrate that memory use with RIPL is comparable to the Vivado HLS OpenCV library without the need for language pragmas to guide hardware synthesis. the benchmarks also show that RIPL is more expressive than the Darkroom FPGA image processing language. © Springer international Publishing AG 2016.

关键词： Image processing

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parallel CLUSTERING BY FAST SEARCH AND FIND OF DENSITY PEAKS

PARALLEL CLUSTERING BY FAST SEARCH AND FIND OF DENSITY PEAKS

引用

5th international conference on Audio, Language and Image processing (ICALIP)

作者： Ji Chengheng Lei Yongmei Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China

ISBN: (纸本)9781509006540

the algorithm clustering by fast search and find of density peaks shows good efficiency and accuracy, but the space complexity of the algorithm is too high since it has to keep a global distance matrix in memory, so it can hardly process big dataset clustering. To solve this problem, this paper designed a new strategy for the algorithm to search the important quantity delta, by using the new strategy, the space complexity of the algorithm is greatly reduced. And based on that reduction, a corresponding load balanced parallel clustering algorithm was presented in this paper, experimental results show that the parallel algorithm is efficient and scalable.

关键词： clustering parallel density K-nearest neighbor

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OPTIMIZATION AND REALIZATION OF parallel FREQUENT ITEM SET MINING ALGORIthM 5

OPTIMIZATION AND REALIZATION OF PARALLEL FREQUENT ITEM SET M...

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5th international conference on Audio, Language and Image processing (ICALIP)

作者： Yuan, Ling Li, Dan Chen, Yuzhong Huazhong Univ Sci & Technol Sch Comp Sci Wuhan 430074 Peoples R China

ISBN: (纸本)9781509006540

Associative data mining is the research hotspot in the field of big data, and frequent item sets mining is an important step in the analysis of associative data. this paper focuses on analyzing the frequent item sets mining algorithm based on Apriori parallel algorithm. the paper has found two shortages of Apriori parallel algorithm: one is that the key value pair are too many, another is that in the combiner stage, it occupies two much memory. therefore, we propose an optimized algorithm. In the optimization algorithm, candidate item sets and local count information are saved in memory, greatly reducing the number of generated keys. Meanwhile, in the short length frequent item sets mining, the method of reducing the number of scanning transaction data without generating candidate item sets can improve the algorithm efficiency. We do the experiments in the Hadoop platform to testify the performance of the proposed optimized algorithm. the experiments demonstrate that the time and I/O of the optimized algorithm have been improved greatly, compared with the non-optimized algorithm.

关键词： Data Mining Frequent item sets Candidate Item Sets Key-value pairs

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