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2017 5th International Conference on Machinery,Materials and Computing Technology(ICMMCT 2017)

作者： Xiaocheng Luo Jingfei Jiang Junjie Zhu Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Audio feature extraction is a very important technique in the field of sound processing. It extremely impacts the effectiveness and correctness of sound recognition, sound verification, etc. It is a computation intensive stage in the whole sound recognition process, which is a challenging for acceleration. In this paper, a coarse-grained parallel feature extraction algorithm for high throughput of audio slices is proposed to improve the efficiency of audio feature extraction. Three typical audio feature extraction algorithms, Mel Frequency Cepstrum Coefficients(MFCC), Spectrogram image features(SIF), Octave-Based Spectral Contrast, are chosen to parallelize. Experiments results on different platforms show that the speedup of accelerated audio feature extraction is up to 17.23 on the platform with 16 cores 32 threads.

关键词： Feature Extraction Sound Recognition parallel algorithm

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A fine-grained parallel algorithm for the cyclic flexible job shop problem

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ARCHIVES OF CONTROL SCIENCES 2017年第2期27卷 169-181页

作者： Bozejko, Wojciech Pempera, Jaroslaw Wodecki, Mieczyslaw Wroclaw Univ Technol Dept Automat Mechatron & Control Syst Fac Elect Janiszewskiego 11-17 PL-50372 Wroclaw Poland Univ Wroclaw Inst Comp Sci Joliot Curie 15 PL-50383 Wroclaw Poland

In this paper there is considered a flexible job shop problem of operations scheduling. The new, very fast method of determination of cycle time is presented. In the design of heuristic algorithm there was the neighborhood inspired by the game of golf applied. Lower bound of the criterion function was used in the search of the neighborhood.

关键词： job shop cyclic scheduling parallel algorithm

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parallel algorithm to Detect Structural Changes in Time Series

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Computational Mathematics and Modeling 2016年第2期27卷 247-253页

作者： Nikol’skii, I.M. Furmanov, K.K. Faculty of Computational Mathematics and Cybernetics Moscow State University Moscow Russian Federation

Analysis of long time series is relevant in many current applications. These investigations are usually carried out on multiprocessor computers (supercomputers). However, supercomputer calculations are efficient only if the time-series processing algorithms are sufficiently parallelized. In this article, we propose a parallel algorithm that detects shift points of the time-series mean — a highly important task in many applications. The algorithm breaks the time series into segments and looks for shift points on each segment using a statistical test. The critical values have been calculated for this test. An additional test reduces the number of false detections. © 2016, Springer Science+Business Media New York.

关键词： detection parallel algorithm structural changepoint time series

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pSIN: A scalable, Parallel algorithm for Seismic INterferometry of large-N ambient-noise data

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COMPUTERS & GEOSCIENCES 2016年 93卷 88-95页

作者： Chen, Po Taylor, Nicholas J. Dueker, Ken G. Keifer, Ian S. Wilson, Andra K. McGuffy, Casey L. Novitsky, Christopher G. Spears, Alec J. Holbrook, W. Steven Univ Wyoming Dept Geol & Geophys Laramie WY 82071 USA

Seismic interferometry is a technique for extracting deterministic signals (i.e., ambient-noise Green's functions) from recordings of ambient-noise wavefields through cross-correlation and other related signal processing techniques. The extracted ambient-noise Green's functions can be used in ambient noise tomography for constructing seismic structure models of the Earth's interior. The amount of calculations involved in the seismic interferometry procedure can be significant, especially for ambient noise datasets collected by large seismic sensor arrays (i.e., "large-N" data). We present an efficient parallel algorithm, named pSIN (parallel Seismic INterferometry), for solving seismic interferometry problems on conventional distributed-memory computer clusters. The design of the algorithm is based on a two-dimensional partition of the ambient-noise data recorded by a seismic sensor array. We pay special attention to the balance of the computational load, inter-process communication overhead and memory usage across all MPI processes and we minimize the total number of I/O operations. We have tested the algorithm using a real ambient-noise dataset and obtained a significant amount of savings in processing time. Scaling tests have shown excellent strong scalability from 80 cores to over 2000 cores. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Seismic interferometry Ambient-noise parallel algorithm Message-passing interface

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An Online parallel algorithm for Recursive Estimation of Sparse Signals

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2016年第3期2卷 290-305页

作者： Yang, Yang Pesavento, Marius Zhang, Mengyi Palomar, Daniel P. Intel Deutschland GmbH D-85579 Neubiberg Germany Tech Univ Darmstadt Commun Syst Grp D-64283 Darmstadt Germany Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong Peoples R China

In this paper, we consider a recursive estimation problem for linear regression where the signal to be estimated admits a sparse representation and measurement samples are only sequentially available. We propose a convergent parallel estimation scheme that consists of solving a sequence of l(1)-regularized least-square problems approximately. The proposed scheme is novel in three aspects: 1) all elements of the unknown vector variable are updated in parallel at each time instant, and the convergence speed is much faster than state-of-the-art schemes which update the elements sequentially;2) both the update direction and stepsize of each element have simple closed-form expressions, so the algorithm is suitable for online(real-time) implementation;and 3) the stepsize is designed to accelerate the convergence but it does not suffer from the common intricacy of parameter tuning. Both centralized and distributed implementation schemes are discussed. The attractive features of the proposed algorithm are also illustrated numerically.

关键词： LASSO linear regression minimization stepsize rule parallel algorithm recursive estimation sparse signal processing stochastic optimization

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An efficient wavefront parallel algorithm for structured three dimensional LU-SGS

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COMPUTERS & FLUIDS 2016年 134卷 23-30页

作者： Gong, Chunye Bao, Weimin Liu, Jie Tang, Guojian Jiang, Yuewen Natl Univ Def Technol Sch Comp Sci Changsha 410073 Hunan Peoples R China Sci & Technol Space Phys Lab Fengtai Sect Beijing 100076 Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

parallel computing is a useful technology for scientific and engineering algorithms/applications. LU-SGS (lower-upper Symmetric-Gauss-Seidel method) is an efficient and robust scheme for CFD (Computational fluid dynamics) and has strong data dependence in its computation. In this paper, we present an efficient wavefront parallel algorithm for 3D (three dimensional) LU-SGS with structured meshes. The corresponding data structure and memory access method with better data locality and communication optimization is designed. The performances of the presented parallel algorithm are reported with different problem sizes. Some discussion and performance issues are also reported. The results show that the overall performance speedup of one Intel E5540 CPU (4 CPU cores) ranges from 2.23 to 2.95 compared with one E5540 core. The parallel efficiency of 1024, 128 processes are up to 35.68%, 72.69% compared with 32 processes on a distributed memory cluster system. The CFD simulation of M6 wing model shows the effect of the presented parallel algorithm. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： CFD LU-SGS parallel computing parallel algorithm 3D structured meshes

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Hybrid swarm intelligent parallel algorithm research based on multi-core clusters

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MICROPROCESSORS AND MICROSYSTEMS 2016年 47卷 151-160页

作者： Li, Wenjing Bi, Yingzhou Zhu, Xiaofeng Yuan, Chang-an Zhang, Xiang-bo Guangxi Teachers Educ Univ Sci Comp & Intelligent Informat Proc GuangXi High Nanning 530023 Guangxi Peoples R China Guangxi Teachers Educ Univ Coll Comp & Informat Engn Nanning 530023 Guangxi Peoples R China Univ N Carolina Dept Radiol Chapel Hill NC USA Univ N Carolina BRIC Chapel Hill NC USA

In order to solve poor fine searching capacity of artificial fish swarm algorithm and artificial bee colony swarm algorithm in late state to result in insufficient local optimization, hybrid swarm intelligent parallel algorithm research based on multi-core clusters is proposed;Then, reverse learning mechanism is introduced in early stage of algorithm, initialized swarms are evenly distributed, and swarms are randomly divided into two groups to make interactive learning strategy accelerates rate of convergence, and basic artificial fish swarm algorithm and artificial bee colony swarm algorithm are used to make global searching. In late stage of algorithm, niches artificial fish swarm algorithm and Random Perturbation Artificial Bee Colony are used to make local fine searching to the solution obtained in early stage;On this basis, MPI+OpenMP+STM parallel programming model based on multi-core clusters is established for parallel design and analysis. Finally, stimulation experiment indicates optimizing efficiency of this algorithm is higher than single artificial fish swarm algorithm and artificial bee colony swarm algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Artificial fish swarm algorithm Artificial bee colony swarm algorithm Optimization Hybrid swarm intelligent algorithm parallel algorithm

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Poisson-Markov Mixture Model and parallel algorithm for Binning Massive and Heterogenous DNA Sequencing Reads 12th

Poisson-Markov Mixture Model and Parallel Algorithm for Binn...

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12th International Symposium on Bioinformatics Research and Applications (ISBRA)

作者： Wang, Lu Zhu, Dongxiao Li, Yan Dong, Ming Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9783319387826;9783319387819

A major computational challenge in analyzing metagenomics sequencing reads is to identify unknown sources of massive and heterogeneous short DNA reads. A promising approach is to efficiently and sufficiently extract and exploit sequence features, i.e., k-mers, to bin the reads according to their sources. Shorter k-mers may capture base composition information while longer k-mers may represent reads abundance information. We present a novel Poisson-Markov mixture Model (PMM) to systematically integrate the information in both long and short k-mers and develop a parallel algorithm for improving both reads binning performance and running time. We compare the performance and running time of our PMM approach with selected competing approaches using simulated data sets, and we also demonstrate the utility of our PMM approach using a time course metagenomics data set. The proba-bilistic modeling framework is sufficiently flexible and general to solve a wide range of supervised and unsupervised learning problems in metagenomics.

关键词： Probabilistic clustering Expectation-Maximization algorithm Metagenomics Next-generation sequencing (NGS) parallel algorithm

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An Accelerated parallel algorithm for Constructing the Nonredundant Matrix of Implications during the Construction of Fault-Tolerant Nonredundant Diagnostic Tests

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AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS 2016年第6期50卷 223-236页

作者： Yankovskaya, A. E. Yamshanov, A. V. Tomsk State Univ Architecture & Bldg Tomsk 634003 Tomsk Oblast Russia Tomsk State Univ Control Syst & Radioelect Tomsk 634050 Tomsk Oblast Russia Tomsk Natl Res Polytech Univ Tomsk 634050 Tomsk Oblast Russia Tomsk Natl Res State Univ Tomsk 634050 Tomsk Oblast Russia

In this article the relevance of using test methods of pattern recognition while developing intelligent systems for decision making support for various problem areas is discussed. The advantage of fault-tolerant diagnostic tests used in intelligent systems is shown, namely, a tool for registering and processing different kinds of errors in databases and knowledge bases. The results of testing two algorithms for constructing the nonredundant matrix of implications are compared;the technical particulars of program implementation are discussed such as synchronization means, test environment, test-program structure, and bottlenecks of program implementation;methods of their elimination, and further development of parallel algorithms.

关键词： parallel algorithm fault-tolerant nonredundant unconditional diagnostic tests decision-making intelligent systems matrix representation of data and knowledge data and knowledge regularities matrix of implications data structures multi-thread programs program performance estimation

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High-Speed Realization of parallel algorithm for Hash Computation On Multi-Core Cryptographic Processor

High-Speed Realization of Parallel Algorithm for Hash Comput...

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IEEE International Conference on Integrated Circuits and Microsystems (ICICM)

作者： Dai, Qiang Dai, Zibin Wang, Zhouchuang Li, Wei Inst Informat Sci & Technol Dept Microelect Zhengzhou Peoples R China Fudan Univ State Key Lab ASIC & Syst Shanghai Peoples R China

ISBN: (纸本)9781509028146

Hashing algorithms are used widely in information security area. Having studied the characteristics of traditional cryptographic hashing function and considered the features of multi-core cryptographic processor, this paper proposes a parallel algorithm for hash computation well-suited to multi core cryptographic processor. The algorithm breaks the chain dependencies of the standard hash function by implementing recursive hash to get faster hash implementation. We discuss the theoretical foundation for our mapping framework including security measure and performance measure. The experiments are performed on a PC with a PCIE card including multi-core cryptographic processor as the cipher processing engine. The results show a performance gain by an approximate factor of 7.8 when running on the 8-core cryptographic processor.

关键词： HASH parallel algorithm multi-core cryptographic processor SM3

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