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检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

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QbDJ: A Novel Framework for Handling Skew in parallel Join processing on Distributed Memory

QbDJ: A Novel Framework for Handling Skew in Parallel Join P...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Long Cheng Spyros Kotoulas Tomas E. Ward Georgios theodoropoulos National University of Ireland Maynooth Ireland IBM Res. Dublin Ireland Durham University UK

the performance of parallel distributed data management systems becomes increasingly important with the rise of Big Data. parallel joins have been widely studied both in the parallel processing and the database communities. Nevertheless, most of the algorithms so far developed do not consider the data skew, which naturally exists in various applications. State of the art methods designed to handle this problem are based on extensions to either of the two prevalent conventional approaches to parallel joins - the hash-based and duplication-based frameworks. In this paper, we introduce a novel parallel join framework, query-based distributed join (QbDJ), for handling data skew on distributed architectures. Further, we present an efficient implementation of the method based on the asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate the performance of our approach on a cluster of 192 cores (16 nodes) and datasets of 1 billion tuples with different skews. the results show that the method is scalable, and also runs faster with less network communication compared to state-of-art PRPD approach in [1] under high data skew.

关键词： Arrays Distributed databases Histograms parallel processing Instruction sets Probes Silicon

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Multi-objective parallel machines scheduling for fault-tolerant cloud systems

Multi-objective parallel machines scheduling for fault-toler...

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13th international conference on algorithms and architectures for parallel processing, ICA3PP 2013

作者： Gasior, Jakub Seredyński, Franciszek Systems Research Institute Polish Academy of Sciences Warsaw Poland Cardinal Stefan Wyszynski University Warsaw Poland

ISBN: (纸本)9783319038582

We present in this paper a security-driven solution for scheduling of N independent jobs on M parallel machines that minimizes three different objectives simultaneously, namely the failure probability, the total completion time of the jobs and their respective tardiness. As this problem is NP-hard in the strong sense, a meta-heuristic method NSGA-II is proposed to solve it. this approach is based on the Pareto dominance relationship, providing no single optimal solution, but a set of solutions which are not dominated by each other. thus, it was necessary to provide decision-making mechanisms selecting the best strategy from the Pareto frontier. the performance of the presented model and the applied GA is verified by a number of numerical experiments. the related results show the effectiveness of the proposed model and GA for small and mediumsized scheduling problems. © Springer international Publishing Switzerland 2013.

关键词： Multiobjective optimization

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Using a Multitasking GPU Environment for Content-Based Similarity Measures of Big Data

Using a Multitasking GPU Environment for Content-Based Simil...

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13th international conference on Computational Science and Its Applications (ICCSA)

作者： Tarakji, Ayman Hassani, Marwan Lankes, Stefan Seidl, thomas Rhein Westfal TH Aachen Chair Operating Syst Aachen Germany

ISBN: (纸本)9783642396403

Performance and efficiency became recently key requirements of computer architectures. Modern computers incorporate Graphics processing Units (GPUs) into running data mining algorithms, as well as other general purpose computations. In this paper, different parallelization methods are analyzed and compared in order to understand their applicability. From multi-threading on shared memory to using NVIDIA's GPU accelerators for increasing performance and efficiency on parallel computing, this work discusses the parallelization of data mining algorithms considering performance and efficiency issues. the performance is compared on both many-core systems and GPU accelerators on a distance measure algorithm using a relatively big data set. We optimize the way we deal with GPUs in heterogeneous systems to make them more suitable for big data mining applications with heavy distance calculations. Moreover, we focus on achieving a higher utilization of GPU resources and a better reuse of data. Our implementation of the content-based similarity algorithm SQFD on the GPU outperforms by up to 50x CPU counterparts, and up to 15x CPU multi-threaded implementations.

关键词： GPGPU Similarity Measures Data Mining Heterogeneous parallel Systems

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RFID based real-time manufacturing information perception and processing

RFID based real-time manufacturing information perception an...

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13th international conference on algorithms and architectures for parallel processing, ICA3PP 2013

作者： Song, Wei Li, Wenfeng Fu, Xiuwen Cao, Yulian Yang, Lin School of Logistics Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9783319038889

Timeliness, accuracy and effectiveness of manufacturing information in manufacturing and business process management have become important factors of constraint to business growth. Single RFID (Radio Frequency Identification) technology with uncertainty will cause great difficulties for application systems. this paper mainly focuses on the process of manufacturing information, real-time information perception and processing problems. It achieves real-time manufacturing information acquisition and processing by combining RFID and sensor technology, which uses Complex Event processing (CEP) mechanism to realize sensor and RFID data fusion. First of all, the event processing framework which is from the perspective of the integration of sensors and RFID is given. then, real-time acquisition and intelligent information processing models were introduced, including primitive event handling and complex event processing method. Finally, the practicality of our method was verified through applying it to a mold manufacturing enterprise management field. © Springer international Publishing Switzerland 2013.

关键词： Internet of things

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Web Information Systems and Technologies - 8th international conference, WEBIST 2012, Revised Selected Papers

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8th international conference on Web Information Systems and Technologies, WEBIST 2012

ISBN: (纸本)9783642366079

the proceedings contain 23 papers. the topics discussed include: efficient parallel algorithms for XML filtering with structural and value constraints;a simulation-based method for eliciting requirements of online CIB systems;reducing latency and network load using location-aware memcache architectures;modeling capabilities as attribute-featured entities;governance policies for verification and validation of service choreographies;real-text dictionary for topic-specific web searching;evaluating cross-platform development approaches for mobile applications;information gathering tasks on the web: attempting to identify the user search behavior;web-based exploration of photos with time and geospace;mixed-initiative management of online calendars;knowledge discovery: data mining by self-organizing maps;and ranking location-dependent keywords to extract geographical characteristics from microblogs.

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parallel B&B Algorithm for Hybrid Multi-core/GPU architectures

Parallel B&B Algorithm for Hybrid Multi-core/GPU Architectur...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： A. Bendjoudi M. Chekini M. Gharbi M. Mehdi K. Benatchba F. Sitayeb-Benbouzid N. Melab CERIST Research Center Algiers Algeria Ecole Suprieur d'Informatique (ESI) Oued Smar Algiers Algeria Université Lille1 France

B&B algorithms are well known techniques for exact solving of combinatorial optimization problems (COP). they perform an implicit enumeration of the search space instead of exhaustive one. Based on a pruning technique, they reduce considerably the computation time required to explore the whole search space. Nevertheless, these algorithms remain inefficient when dealing with large combinatorial optimization instances. they are time-intensive and they require a huge computing power to be solved optimally. Nowadays, multi-core-based processors and GPU accelerators are often coupled together to achieve impressive performances. However, classical B&B algorithms must be rethought to deal with their two divergent architectures. In this paper, we propose a new B&B approach exploiting both the multi-core aspect of actual processors and GPU accelerators. the proposed approaches have been executed to solve FSP instances that are well-known combinatorial optimization benchmarks. Real experiments have been carried out on an Intel Xeon 64-bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. the results show that our hybrid B&B approach speeds up the execution time up to ×123 over the sequential mono-core B&B algorithm.

关键词： Graphics processing units Upper bound Search problems parallel processing Multicore processing

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A parallel distributed system for gene expression profiling based on clustering ensemble and distributed optimization

A parallel distributed system for gene expression profiling ...

引用

13th international conference on algorithms and architectures for parallel processing, ICA3PP 2013

作者： Benmounah, Zakaria Batouche, Mohamed Computer Science Department College of NTIC Constantine University 2 25000 Constantin Algeria

ISBN: (纸本)9783319038582

With the development of microarray technology, it is possible now to study and measure the expression profiles of thousands of genes simultaneously which can lead to identify subgroup of specific disease or extract hidden relationships between genes. One computational method often used to this end is clustering. In this paper, we propose a parallel distributed system for gene expression profiling (PDS-GEF) which provides a useful basis for individualized treatment of a certain disease such as Cancer. the proposed approach is based on two major techniques: the GIM (Generalized Island Model) and clustering ensemble. GIMs are used to generate good quality clusterings which are refined by a consensus function to get a high quality clustering. PDS-GEF system is implemented using Matlab®'s PCT (parallel Computing Toolbox™) which runs on a desktop computer, and tested on 34 different publicly available gene expression data sets. the obtained results compete with and even outperform existing methods. © Springer international Publishing Switzerland 2013.

关键词： Diseases

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Performance enhancement of map-reduce framework on GPU

Performance enhancement of map-reduce framework on GPU

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11th IASTED international conference on parallel and Distributed Computing and Networks, PDCN 2013

作者： Parakh, Arun Kumar Balakrishnan, Meenakshi Paul, Kolin Department of Computer Science and Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi India

ISBN: (纸本)9780889869431

the objective of this work is to get benefit of advancement in GPU technologies in the state of art software framework. We have analyzed the existing map-reduce (MR) framework and modify the same for new GPU architectures. We have identified some significant possibilities for improvement. these improvements are mainly in the context of the different GPU architectures, which were introduced after the development of the MR framework. Our experiments show an average of 2.5x speedup of MR framework on these architectures. Cache reconfiguration is also investigated in this work. We have achieved performance benefit ranging from 10% to 200% for various cache sizes. Based on the above analysis, three techniques have been developed for the performance enhancement of MR framework. First, we exploited the concept of principle of locality by code restructure. We have saved over 32%cache miss per thread. Second, we have reduced the number of comparisons per thread in group phase. Our optimized group phase gives an average of 1.5x speed up. In third optimization, we have performed delayed writing during mapperCount function and make this function as cache sensitive. this reduces significant cache misses and improves the execution time by 10% to 25% for this function.

关键词： Graphics processing unit

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Advanced algorithms and their parallel architectures for full-HD TV video display processing 13

Advanced algorithms and their parallel architectures for ful...

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7th international conference on Ubiquitous Information Management and Communication, ICUIMC 2013

作者： Kim, Young-Chul Jo, Jung-Jae Dept. of Electronic and Computer Engineering Chonnam National University Gwangju Korea Republic of

ISBN: (纸本)9781450319584

this Paper proposes an effective SoC hardware architecture implementing a VDP for Full HD TVs. the proposed architecture makes real time video processing possible with supporting efficient bus architecture and flexible interface. Video IP cores in the VDP are designed to provide a high quality of improved image enhancement function. the Avalon interface is adopted to guarantee real-time capability to IPs as well as SoC integration. this leads to reduced design time and also enhanced designer's convenience due to the easiness in IP addition, deletion, and revision for IP verification and SoC integration. the embedded software makes it possible to implement flexible real-time system by controlling setting parameter details and data transmitting schemes in real-time. the proposed VDP SoC design is implemented on Cyclon III SoPC platform. the experimental results show that our proposed architecture of the VDP SoC successfully provides required quality of video image by converting SD level input to Full HD level image. Copyright © 2013 ACM.

关键词： Real time systems

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parallelization and Optimization of SIFT on GPU Using CUDA

Parallelization and Optimization of SIFT on GPU Using CUDA

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15th IEEE international conference on High Performance Computing and Communications (HPCC) /11th IEEE/IFIP international conference on Embedded and Ubiquitous Computing (EUC)

作者： Zhou, Yonglong Mei, Kuizhi Ji, Xiang Dong, Peixiang Xi An Jiao Tong Univ Xian 710049 Peoples R China

ISBN: (纸本)9780769550886

Scale-invariant feature transform (SIFT) based feature extraction algorithm is widely applied to extract features from images, and it is very attractive to accelerate these SIFT based algorithms on GPU. In this paper, we present several parallel computing strategies, implement and optimize the SIFT algorithm using CUDA programming model on GPU. Each stage of SIFT is analyzed in detail to choose the parallel strategy. On the basis of the elementary CUDA-SIFT and CUDA architecture, we optimize the implementation from several aspects to speedup the CUDA-SIFT. Experimental results demonstrate that our implementation after optimization is 2.5 times faster than previous optimization, and our CUDA based SIFT can run at the speed of 20 frames per second on most images with 1280x960 resolution in the test. Using 1920x1440 image to test, we have obtained a speed of 11 frames per second on average, which is about 60 times faster than the CPU implementation of SIFT. In short, our implementation obtains appropriate accuracy and higher efficiency compared to CPU implementations and other GPU implementations, which is attributed to our dedicated optimization strategies.

关键词： Graphics processing units Instruction sets Feature extraction Convolution Histograms Optimization Vectors

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