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

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Improving performance of floating point division on GPU and MIC 15th

Improving performance of floating point division on GPU and ...

引用

15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

作者： Huang, Kun Chen, Yifeng Department of Computer Science School of EECS Peking University Beijing100871 China

ISBN: (纸本)9783319271217

Floating point computing ability is an important concern in high performance scientific application and engineering computing. Although as a fundamental operation, floating point division (or reciprocal) has long been much less efficiency compared with addition and multiplication. architectures like GPU and MIC even have no instruction for such division in the instruction level. this paper proposes a fast approximation algorithm to estimate the division of floating point numbers in IEEE 754 format based on existing instructions which in most cases are accurate enough for practical computing. It consists of a predicting step and an iterating step like most iterative numerical algorithm. the predicting step makes use of the property of IEEE 754 format to calculate estimation by only one integer subtraction instruction. the iterating step improves the accuracy by fast iterations in about ten instructions. this new algorithm is extremely easy to implement and shows a great performance in practical experiments. © Springer international Publishing Switzerland 2015.

关键词： Graphics processing unit

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Multithreaded local learning regularization neural networks for regression tasks 16th

Multithreaded local learning regularization neural networks ...

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16th international conference on Engineering Applications of Neural Networks, EANN 2015

作者： Kokkinos, Yiannis Margaritis, Konstantinos G. Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia 156 Egnatia Str. P. O. Box 1591 Thessaloniki54006 Greece

ISBN: (纸本)9783319239811

We explore four local learning versions of regularization networks. While global learning algorithms create a global model for all testing points, the local learning algorithms use neighborhoods to learn local parameters and create on the fly a local model specifically designed for any particular testing point. this approach delivers breakthrough performance in many application domains. Usually however the computational overhead is substantial, and in some cases prohibited. For speeding up the online predictions we exploit both multithreaded parallel implementations as well as interplay between locally optimized parameters and globally optimized parameters. the multithreaded local learning regularization neural networks are implemented with OpenMP. the accuracy of the algorithms is tested against several benchmark datasets. the parallel efficiency and speedup is evaluated on a multi-core system. © Springer international Publishing Switzerland 2015.

关键词： Learning algorithms

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A mobile SoC-based platform for evaluating hearing aid algorithms and architectures

A mobile SoC-based platform for evaluating hearing aid algor...

引用

IEEE international conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Christopher Seifert Guillermo Payá-Vayá Holger Blume Tobias Herzke Volker Hohmann Institute of Microelectronic Systems Leibniz Universitat Hannover and Cluster of Excellence Hearing4all Appelstr Hannover Germany Hortech gGmbH Marie-Curie-Str Med. Physics Carl von Ossietzky-Universitat Oldenburg and Cluster of Excellence Hearing4all Oldenburg Germany

Current research on audio signal processing algorithms for digital hearing aid devices is extremely pushing the performance demands. Nowadays, there is a trend of using several microphones in such systems (e.g., binaural systems) to improve the speech perception of a hearing impaired person. However, there is a lack of mobile platforms, capable of processing such algorithms in real-time. this paper presents a new mobile SoC-based evaluation and development platform (including a multi-channel audio extension board), specially thought not only for evaluating new hearing aid signal processing algorithms but also to develop new hardware co-processor architectures, that could be integrated in current hearing aid devices to improve their performance with a minimal extra energy consumption.

关键词： Auditory system Signal processing algorithms Hardware IP networks Random access memory Program processors Mobile communication

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PE-TLD: parallel extended tracking-learning-detection for multi-target tracking 15th

PE-TLD: Parallel extended tracking-learning-detection for mu...

引用

15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

作者： Zhou, Chenggang Dong, Qiankun Ma, Wenjing Long, Guoping Li, Tao College of Computer and Control Engineering Nankai University Tianjin300350 China Institute of Software Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319271217

Multi-target tracking in video has been a research focus, with the combination of many fields, such as computer vision, artificial intelligence, pattern matching. In this paper, we present an efficient multi-target recognition and tracking algorithm based on TLD (Tracking-Learning-Detection), named PETLD. A new foreground extraction filter using ViBe is introduced to improve the speed and accuracy of detection. A new target recognition component is added, and core detector is improved. Based on that, we further implemented a parallel version, taking advantage of the state-of-the-art parallel computing techniques such as OpenMP and OpenCL, which runs efficiently on a system with both multicore CPU and GPU. Experiments showed that PE-TLD is up to 5 times faster than the serial version. PE-TLD is an automatic multi-target recognition and tracking system, which is efficient enough to be deployed for real-time usage. © Springer international Publishing Switzerland 2015.

关键词： Pattern matching

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Performance analysis for job scheduling in hierarchical HPC systems: A coloured petri nets method 15th

Performance analysis for job scheduling in hierarchical HPC ...

引用

15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

作者： Li, Zhijia Jiao, Li Hu, Xiang State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783319271392

Distributed computing technology has been widely used to solve complex problems appearing in parallel processing systems. Job scheduling is very important in many distributed computing systems, like grid systems and high performance computers. their performance is directly related to the efficiency of the distributed computing systems. Modeling them and analyzing their performance can provide quantitative performance metrics and predictions, which are helpful to guide capacity planning and scheduling optimization. In this paper, we study job scheduling systems widespread in high performance computing systems and propose a coloured Petri net method for analyzing their performance, which can be easily implemented in CPN software by potential users. We also propose an approximative modeling technique so as to reduce the model size. As a model-based performance analysis method, our method is low cost and highly flexible. Experimental results show that our method is feasible and can be applied to more complex and large-scale systems. © Springer international Publishing Switzerland 2015.

关键词： parallel processing systems

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An Improved Algorithm for Querying Encrypted Data in the Cloud 5

An Improved Algorithm for Querying Encrypted Data in the Clo...

引用

5th international conference on Communication Systems and Network Technologies (CSNT)

作者： Shastri, Samraddhi Kresman, Ray Lee, Jong Kwan Bowling Green State Univ Dept Comp Sci Bowling Green OH 43403 USA

ISBN: (纸本)9781479917976

Organizations have begun outsourcing management of their data to third party cloud service providers after the introduction of Database as a Service (DAS) model. A cloud database is a database that typically runs on a cloud computing platform, such as Amazon EC2, GoGrid, Salesforce and Rackspace. But outsourcing the data raises concerns over privacy. A typical solution is to store databases in encrypted form on the remote server. Queried records are downloaded from the server and decrypted for further processing. Bucketization is one technique for executing queries over encrypted data on a DAS server. this paper is an extension to work done by other researchers [1-4]. Query Optimal Bucketization (QOB) algorithm [1-2] divides the server data into buckets subject to an optimality constraint. In an earlier paper [3], the authors proposed Binary Query Bucketization (BQB) to improve the search time for bucketized datasets and reduce the number of records that are processed by QOB. In this paper, we propose a parallel Binary Query Bucketization (PBQB) algorithm to query records located in the DAS. It integrates parallel search [4] and BQB. parallel search divides the search workload into chunks with each thread/processor working on a chunk. Simulation is used to assess the numerical performance of PBQB. It is shown that the proposed algorithm outperforms BQB.

关键词： parallel Bucketization Encrypted Database Database as a Service

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Optimal Performance Prediction of ADAS algorithms on Embedded parallel architectures

Optimal Performance Prediction of ADAS Algorithms on Embedde...

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

作者： Romain Saussard Boubker Bouzid Marius Vasiliu Roger Reynaud Renault S.A.S Guyancourt France Instutut d'Electronique Fondamentale Université Paris Sud Orsay France

ADAS (Advanced Driver Assistance Systems) algorithms increasingly use heavy image processing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. these SoCs (System on Chip) are composed of different processing units, with different capabilities, and often with massively parallel computing unit. Due to the complexity of these SoCs, predicting if a given algorithm can be executed in real time on a given architecture is not trivial. In fact it is not a simple task for automotive industry actors to choose the most suited heterogeneous SoC for a given application. Moreover, embedding complex algorithms on these systems remains a difficult task due to heterogeneity, it is not easy to decide how to allocate parts of a given algorithm on the different computing units of a given SoC. In order to help automotive industry in embedding algorithms on heterogeneous architectures, we propose a novel approach to predict performances of image processing algorithms applicable on different types of computing units. Our methodology is able to predict a more or less wide interval of execution time with a degree of confidence using only high level description of algorithms, and a few characteristics of computing units.

关键词： Kernel Computer architecture Image processing Graphics processing units Prediction algorithms parallel processing Computational modeling

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parallel column subset selection of kernel matrix for scaling up support vector machines 15th

Parallel column subset selection of kernel matrix for scalin...

引用

15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

作者： Wu, Jiangang Feng, Chang Gao, Peihuan Liao, Shizhong School of Computer Science and Technology Tianjin University Tianjin300072 China

ISBN: (纸本)9783319271361

Nyström method and low-rank linearized Support Vector Machines (SVMs) are two widely used methods for scaling up kernel SVMs, both of which need to sample part of columns of the kernel matrix to reduce the size. However, existing non-uniform sampling methods suffer from at least quadratic time complexity in the number of training data, limiting the scalability of kernel SVMs. In this paper, we pro- pose a parallel sampling method called parallel column subset selection (PCSS) based on the divide-and-conquer strategy, which divides the kernel matrix into several small submatrices and then selects columns in parallel. We prove that PCSS has a (1+ϵ) relative-error upper bound with respect to the kernel matrix. Further, we present two approaches to scaling up kernel SVMs by combining PCSS with Nyström method and lowrank linearized SVMs. the results of comparison experiments demonstrate the effectiveness, efficiency and scalability of our approaches. © Springer international Publishing Switzerland 2015.

关键词： Support vector machines

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A cyber physical system with gpu for CNC applications 15th

A cyber physical system with gpu for CNC applications

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15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

作者： Chang, Jen-Chieh Chien, Ting-Hsuan Chang, Rong-Guey Department of Computer Science and Information Engineering National Chung Cheng University Chiayi62102 Taiwan

ISBN: (纸本)9783319271361

In this paper, we parallelize the collision detection of five- axis machining as an example to show how to execute CNC applications on Graphics processing Unit (GPU). We first design and implement an efficient collision detection tool, including the kinematics analyses for five-axis motions, separating axis method for collision detection, and computer simulation for verification. the machine structure is modeled as STL format in CAD software. the input to the detection system is the g-code part program, which describes the tool motions to produce the part surface. then the g-code will be partitioned and be executed by our collision detection tool in parallel on Graphics processing Unit (GPU). the system simulates the five-axis CNC motion for tool trajectory and detects any collisions according to the input g-codes. the result shows that our method can improve the performance of computational efficiency significantly when comparing to the conventional detection method. © Springer international Publishing Switzerland 2015.

关键词： Graphics processing unit

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Shortest-Path Queries in Planar Graphs on GPU-Accelerated architectures 10th

Shortest-Path Queries in Planar Graphs on GPU-Accelerated Ar...

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10th international conference on Large-Scale Scientific Computations (LSSC)

作者： Chapuis, Guillaume Djidjev, Hristo Los Alamos Natl Lab POB 1663 Los Alamos NM 87545 USA

ISBN: (纸本)9783319265209;9783319265193

We develop an efficient parallel algorithm for answering shortest-path queries in planar graphs and implement it on a multi-node CPU-GPU clusters. the algorithm uses a divide-and-conquer approach for decomposing the input graph into small and roughly equal subgraphs and constructs a distributed data structure containing shortest distances within each of those subgraphs and between their boundary vertices. For a planar graph with n vertices, that data structure needs O(n) storage per processor and allows queries to be answered in O(n(1/4)) time.

关键词： Shortest path problems Graph algorithms Distributed computing GPU computing Graph partitioning

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