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14 International Symposium on Integrated Circuits (ISIC)

作者： Sudha, N. Sridharan, K. Wilkinson, Dan XMOS Semicond India Pvt Ltd Madras Tamil Nadu India Indian Inst Technol Madras Dept Elect Engn Madras Tamil Nadu India XMOS Ltd Bristol Avon England

ISBN: (纸本)9781479948338

Motion tracking is an important task in computer vision applications. Embedded system design for this task needs to address goals of high performance with low area and power requirements. In this paper, we propose a pipelined parallel architecture for motion tracking that is appropriate for implementation on a multicore environment. The architecture comprises of modules for frame subtraction, binarization, morphological operations and connected component analysis operating in sequence on an image frame. Successive frames are processed in a pipeline on multicores. Embedded system realization on a multicore XMOS microcontroller runs the drivers for interfacing image sensor, SDRAM and image sensor on different cores along with the various stages of the image processing pipeline. The realization achieves a frame rate of 8 fps for an image size of 480x272. Further, the solution is area-efficient and is based on a single XMOS sliceKIT with support (in the form of compact slices) for camera, LCD, memory and other units.

关键词： motion tracking pipelined architecture multicore processor parallel algorithm XMOS sliceKIT

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Research and Implementation of Petri nets parallelization model 13

Research and Implementation of Petri nets parallelization mo...

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13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Wang, Xuan Li, Wen-jing Tang, Ze-yu Liao, Weizhi Guangxi Teachers Educ Univ Coll Comp & Informat Engn Nanning 530001 Peoples R China Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

ISBN: (纸本)9781479941698

In order to solve parallel algorithm of Petri net system with concurrent functions and implement parallel control and execution of Petri net, parallel programming model of Petri net based on multi-core clusters is put forward. First, P-invariant technology is used to do the functional division of Petri net system and the parallel analysis of Petri net process. Next, based on architecture of multi-core cluster and combined with parallelism of Petri net process, Petri net system model is constructed. Simultaneously, parallel algorithm of Petri net system is raised. Finally, under the environment of multi-core cluster, the simulation experiment of parallel algorithm is done. The experimental results show that parallel algorithm based on Petri net system model is correct and feasible. The model can effectively simulate the actual operation of the system and is an effective model of Petri net parallelization system.

关键词： Petri Net parallelism Multi-core Cluster parallel Model parallel algorithm

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Classification via Nearest Prototype Classifier Utilizing Artificial Bee Colony on CUDA

Classification via Nearest Prototype Classifier Utilizing Ar...

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International Joint Conference on SOCO, CISIS, ICEUTE

作者： Janousesek, Jan Gajdos, Petr Radecky, Michal Snasel, Vaclav VSB Tech Univ Ostrava FEECS Dept Comp Sci Ostrava 70833 Czech Republic

ISBN: (纸本)9783319079950;9783319079943

Artificial bee colony is a metaheuristic optimization algorithm based on the behaviour of honey bee swarm. These bees work largely independently of other bees, making the algorithm suitable for parallel implementation. Within this paper, we introduce the algorithm itself and its subsequent parallelization utilizing the CUDA platform. The runtime speedup is demonstrated on several commonly used test functions for optimization. The algorithm is subsequently applied to the problem of classifying real data.

关键词： parallel algorithm artificial bee colony CUDA nearest prototype classifier classification

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Enumerating Maximal Bicliques from a Large Graph using MapReduce 3

Enumerating Maximal Bicliques from a Large Graph using MapRe...

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3rd IEEE International Congress on Big Data

作者： Mukherjee, Arko Provo Tirthapura, Srikanta Iowa State Univ Ames IA 50011 USA

ISBN: (纸本)9781479950577

We consider the enumeration of maximal bipartite cliques (bicliques) from a large graph, a task central to many practical data mining problems in social network analysis and bioinformatics. We present novel parallel algorithms for the MapReduce platform, and an experimental evaluation using Hadoop MapReduce. Our algorithm is based on clustering the input graph into smaller sized subgraphs, followed by processing different subgraphs in parallel. Our algorithm uses two ideas that enable it to scale to large graphs: (1) the redundancy in work between different subgraph explorations is minimized through a careful pruning of the search space, and (2) the load on different reducers is balanced through the use of an appropriate total order among the vertices. Our evaluation shows that the algorithm scales to large graphs with millions of edges and tens of millions of maximal bicliques. To our knowledge, this is the first work on maximal biclique enumeration for graphs of this scale.

关键词： Graph Mining Maximal Biclique Enumeration MapReduce Hadoop parallel algorithm Biclique

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Performance Evaluation of Long Integer Multiplication Using OpenMP and MPI on Shared Memory Architecture 7

Performance Evaluation of Long Integer Multiplication Using ...

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7th International Conference on Contemporary Computing (IC3)

作者： Tembhurne, Jitendra V. Sathe, Shailesh R. Visvesvaraya Natl Inst Technol Deptartment Comp Sci & Engn Nagpur Maharashtra India

ISBN: (纸本)9781479951734

Multiplying long integers is an operation that has many applications in Computational Science and arbitrary arithmetic computation. Many cryptographic algorithms such as Diffe-Hellman key exchange [1], RSA, ECC and etc. require operations on very large subsets of the integer numbers. These algorithms use security key size of 1024 bits and more for secure message encryption, decryption and, key exchange. To perform encryption, decryption and key exchange we are required to perform a series of exponentiation and multiplication operations. In this paper, we propose the parallel multiplication algorithms for long integers on multiple core processors using parallel programming languages such as OpenMP and MPI. The OpenMP and MPI are most common parallel languages for programming and provide different performances characteristics. We also target to analyze and compare the OpenMP and MPI implementations on multi-core processors. The new proposed parallel algorithm achieves better performance than the basic serial implementations. The achieved speedup by OpenMP and MPI on serial implementation are 12.62x and 10.72x using school-book, 49.95x and 37.26x using Karatsuba on school-book, 66.11x and 31.47x using Toom-Cook on school-book. We have observed, as bit-length are increased the parallel algorithms exhibits better speedup performance on utilizing more cores available.

关键词： Long number multiplication OpenMP MPI performance evaluation parallel algorithm

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Clustering using Artificial Bee Colony on CUDA

Clustering using Artificial Bee Colony on CUDA

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Janousek, Jan Platos, Jan Snasel, Vaclav Tech Univ Ostrava Dept Comp Sci VSB FEECSInnovat IT4 17 Listopadu 15 Ostrava 70833 Czech Republic

ISBN: (纸本)9781479938407

Artificial bee colony is a meta-heuristic optimization algorithm based on the behavior of honey bee swarm. These bees work largely independently of other bees, making the algorithm suitable for parallel implementation. Within this paper, we introduce the algorithm itself and its subsequent parallelization utilizing the CUDA platform. The runtime speedup is demonstrated on several commonly used test functions for optimization. The algorithm is subsequently applied to the problem of clustering real data.

关键词： parallel algorithm artificial bee colony CUDA clustering

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ParK: An Efficient algorithm for k-core Decomposition on Multicore Processors 2

ParK: An Efficient Algorithm for <i>k</i>-core Decomposition...

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IEEE International Conference on Big Data

作者： Dasari, Naga Shailaja Desh, Ranjan Zubair, M. Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA

ISBN: (纸本)9781479956661

The k-core of a graph is the largest induced subgraph with minimum degree k. The k-core decomposition is to find the core number of each vertex in a graph, which is the largest value of k that the vertex belongs to a k core. k-core decomposition has applications in many areas including network analysis, computational biology and graph visualization. The primary reason for it being widely used is the availability of an O(n + m) algorithm. The algorithm was proposed by Batagelj and Zaversnik and is considered the state-of-the-art algorithm for k-core decomposition. However, the algorithm is not suitable for parallelization and to the best of our knowledge there is no algorithm proposed for k-core decomposition on multicore processors. Also, the algorithm has not been experimentally analyzed for large graphs. Since the working set size of the algorithm is large, and the access pattern is highly random, it can be inefficient for large graphs. In this paper, we present an experimental analysis of the algorithm of Batagelj and Zaversnik and propose a new algorithm, ParK, that significantly reduces the working set size and minimizes the random accesses. We provide an experimental analysis of the algorithm using graphs with up to 65 million vertices and 1.8 billion edges. We compare the ParK algorithm with state-of-the-art algorithm and show that it is up to 6 times faster. We also provide a parallel methodology and show that the algorithm is amenable to parallelization on multicore architectures. We ran our experiments on a 4 socket Nehalem-EX processor which has 8 cores per socket and show that the algorithm scales up to 21 times using 32 cores.

关键词： k-core parallel algorithm multicore graph decomposition

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pbitMCE: A bit-based approach for Maximal Clique Enumeration on Multicore Processors 20

<i>pbitMCE</i>: A bit-based approach for Maximal Clique Enum...

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20th IEEE International Conference on parallel and Distributed Systems (ICPADS)

作者： Dasari, Naga Shailaja Desh, Ranjan Zubair, M. Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA

ISBN: (纸本)9781479976157

Maximal clique enumeration (MCE) is a fundamental problem in graph theory. It plays a vital role in many network analysis applications and in computational biology. MCE is an extensively studied problem. Recently, Eppstein et al. proposed a state-of-the-art sequential algorithm that uses degeneracy based ordering of vertices to improve the efficiency. In this paper, we propose a new parallel implementation of the algorithm of Eppstein et al. using a new bit-based data structure. The new data structure not only reduces the working set size significantly but also by enabling the use of bit-parallelism improves the performance of the algorithm. We illustrate the significance of degeneracy ordering in load balancing and experimentally evaluate the impact of scheduling on the performance of the algorithm. We present experimental results on several types of synthetic and real-world graphs with up to 50 million vertices and 100 million edges. We show that our approach outperforms Eppstein et al.'s approach by up to 4 times and also scales up to 29 times when run on a multicore machine with 32 cores.

关键词： maximal clique bit-parallel multicore degeneracy parallel algorithm

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K-medoids Clustering Based on MapReduce and Optimal Search of Medoids 9

K-medoids Clustering Based on MapReduce and Optimal Search o...

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9th IEEE International Conference on Computer Science and Education (ICCSE)

作者： Zhu, Ying-ting Wang, Fu-zhang Shan, Xing-hua Lv, Xiao-yan China Acad Railway Sci Railway Technol Res Coll Beijing Peoples R China China Acad Railway Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9781479929511

When there are noises and outliers in the data, the traditional k-medoids algorithm has good robustness, however, that algorithm is only suitable for medium and small data set for its complex calculation. MapReduce is a programming model for processing mass data and suitable for parallel computing of big data. Therefore, this paper proposed an improved algorithm based on MapReduce and optimal search of medoids to cluster big data. Firstly, according to the basic properties of triangular geometry, this paper reduced calculation of distances among data elements to help search medoids quickly and reduce the calculation complexity of k-medoids. Secondly, according to the working principle of MapReduce, Map function is responsible for calculating the distances between each data element and medoids, and assigns data elements to their clusters;Reduce function will check for the results from Map function, search new medoids by the optimal search strategy of medoids again, and return new results to Map function in the next MapReduce process. The experiment results showed that our algorithm in this paper has high efficiency and good effectiveness.

关键词： MapReduce k-medoids parallel algorithm cluster analysis data mining

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parallel algorithm Research on Several Important Open Problems in Bioinformatics

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2009年第3期1卷 187-195页

作者： Niu, Bei-Fang Lang, Xian-Yu Lu, Zhong-Hua Chi, Xue-Bin Chinese Acad Sci Super Comp Ctr Comp Network Informat Ctr Beijing 100190 Peoples R China Chinese Acad Sci Grad Univ Beijing 100039 Peoples R China

High performance computing has opened the door to using bioinformatics and systems biology to explore complex relationships among data, and created the opportunity to tackle very large and involved simulations of biological systems. Many supercomputing centers have jumped on the bandwagon because the opportunities for significant impact in this field is infinite. Development of new algorithms, especially parallel algorithms and software to mine new biological information and to assess different relationships among the members of a large biological data set, is becoming very important. This article presents our work on the design and development of parallel algorithms and software to solve some important open problems arising from bioinformatics, such as structure alignment of RNA sequences, finding new genes, alternative splicing, gene expression clustering and so on. In order to make these parallel software available to a wide audience, the grid computing service interfaces to these software have been deployed in China National Grid (CNGrid). Finally, conclusions and some future research directions are presented.

关键词： parallel algorithm SiClone alternative splicing gene expression grid

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