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Efficient parallel algorithm for detecting influential nodes in large biological networks on the Graphics Processing Unit

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 106卷 1-13页

作者： Xiao, Lei Wang, Shuangyan Mei, Gang China Univ Geosci Beijing Sch Engn & Technol Beijing 100083 Peoples R China

In biological networks, some nodes are more influential than others. The most influential nodes are those whose elimination induces a network collapse, and detecting these nodes is crucial in many circumstances. However, this is a difficult task when the size of the biological networks is large. In this paper, we have designed and implemented an efficient parallel algorithm for detecting influential nodes for large biological networks by exploiting a Graphics Processing Unit (GPU). The essential concept behind the proposed parallel algorithm is that several computationally expensive procedures in detecting influential nodes are redesigned and transformed into quite efficient GPU-accelerated primitives such as parallel sort, scan, and reduction. Four local metrics, including the Degree Centrality (DC), Companion Behavior (CB), Clustering Coefficient (CC), and H-Index, are used to measure the nodal influence. To evaluate the efficiency of the proposed parallel algorithm, five large real biological networks are employed in the experiments. The experimental results show that (1) the proposed parallel algorithm can achieve speedups of approximately 48 similar to 94 over the corresponding serial algorithm;(2) compared to a baseline parallel algorithm developed on a multi-core CPU, the proposed parallel algorithm yields speedups of 5 similar to 9 for DC and H-Index, while it is slightly slower for CB and CC due to the uneven degree distribution;and (3) when using DC and H-Index, the proposed parallel algorithm is capable of detecting the influential nodes in a large biological network consisting of 150 million edges in less than 3 s. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Biological networks Influential nodes Degree Centrality Clustering Coefficient H-Index parallel algorithm

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Accelerating computed tomographic imaging spectrometer reconstruction using a parallel algorithm exploiting spatial shift-invariance

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OPTICAL ENGINEERING 2020年第5期59卷 55110-55110页

作者： White, Larz Bell, W. Bryan Haygood, Ryan Lockheed Martin Aeronaut Ft Worth TX 76108 USA

Computed tomographic imaging spectrometers capture hyperspectral images in real-time. However, postprocessing the imagery can require enormous computational resources;thus, limiting its application to nonrealtime scenarios. To overcome these challenges, we developed a highly parallelizable algorithm that exploits spatial shift-invariance. To demonstrate the versatility of our algorithm, we developed implementations on a desktop and an embedded graphics processing unit. To our knowledge, our results show the fastest image reconstruction times reported. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： GPU CUDA CTIS computed tomography imaging spectrometer parallel algorithm

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The evolution model for big data storage structure of online learning behaviour based on parallel algorithm

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2020年第4期13卷 431-447页

作者： Zhou, Ying Nantong Normal Coll Sch Informat & Technol Nantong 226001 Peoples R China

In order to overcome the heavy task of big data storage structure evolution computation, this paper proposes a parallel algorithm based network learning behaviour big data storage structure evolution model. This method introduces parallel algorithm, divides the whole dataset into several non overlapping data subsets randomly, mines the local frequent itemsets in the network learning behaviour big data in parallel and hierarchically, and connects the local frequent itemsets. Frequent itemsets can get all candidate sets. The actual support degree of different candidate sets is calculated by scanning datasets, and the evolution model of big data storage structure of network learning behaviour is established. The experimental results show that the operation efficiency of the proposed evolutionary model is as high as 99%, the cost is significantly lower than the other three evolutionary models, and the storage space consumption is the lowest.

关键词： online learning behaviour big data storage structure evolution model parallel algorithm candidate sets data subsets randomly

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Scalable parallel algorithm for Solving Non-stationary Systems of Linear Inequalities

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LOBACHEVSKII JOURNAL OF MATHEMATICS 2020年第8期41卷 1571-1580页

作者： Sokolinsky, L. B. Sokolinskaya, I. M. Natl Res Univ South Ural State Univ Chelyabinsk 454080 Russia

In this paper, a scalable iterative projection-type algorithm for solving non-stationary systems of linear inequalities is considered. A non-stationary system is understood as a large-scale system of inequalities in which coefficients and constant terms can change during the calculation process. The proposed parallel algorithm uses the concept of pseudo-projection which generalizes the notion of orthogonal projection. The parallel pseudo-projection algorithm is implemented using the parallel BSF-skeleton. An analytical estimation of the algorithm scalability boundary is obtained on the base of the BSF cost metric. The large-scale computational experiments were performed on a cluster computing system. The obtained results confirm the efficiency of the proposed approach.

关键词： non-stationary system of linear inequalities feasibility problem iterative method parallel algorithm approximate solution cluster computing system

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Design and implementation of parallel algorithm for image matching based on Hausdorff Distance

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MICROPROCESSORS AND MICROSYSTEMS 2021年 82卷 103919-103919页

作者： Liu, Qiong Peng, Hao Chen, Jifeng Gao, Haibo Hunan Int Econ Univ Changsha 410205 Hunan Peoples R China

Object matching two-dimensional images in computer vision has become a significant subject of article acknowledgment and picture investigation. Hausdorff Distance assumes a significant function in coordinating image. To proposed system parallel algorithm for image matching manage the instance of arbitrary commotion, image coordinating, new Hausdorff Distance is proposed in this. In contrast to coordinating two twofold pictures, different techniques, the proposed strategy might be coordinated with a few dark scale image pixel esteems. One case of article acknowledgment is utilized to show the productivity of the proposed strategy. The outcomes demonstrated that, contrasted and, the new Hausdorff Distance (HD) might be more alluring approach to discard image clamor coordinating, because of the extensive reflection to decide the dark scale data Hausdorff Distance of adjoining pixels in the shooting of his realities into account. Besides, the strategy can be acknowledged in a straightforward manner.

关键词： Hausdorff Distance (HD) parallel algorithm Two-dimensional images Image matching Computer vision

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Generating Minimal Nondeterministic Finite Automata Using a parallel algorithm 19

Generating Minimal Nondeterministic Finite Automata Using a ...

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19th International Symposium on parallel and Distributed Computing (ISPDC)

作者： Jastrzab, Tomasz Czech, Zbigniew J. Wieczorek, Wojciech Silesian Tech Univ Dept Algorithm & Software Gliwice Poland Silesia Univ Katowice Fac Sci & Technol Katowice Poland

ISBN: (纸本)9781728189468

The goal of this paper is to develop a parallel algorithm that, on input of a learning sample, identifies a regular language by means of a nondeterministic finite automaton (NFA). A sample is a pair of finite sets containing positive and negative examples. Given a sample, a minimal NFA or the range of possible sizes of such an NFA, that represents the target regular language is sought. We define the task of finding an NFA, which accepts all positive examples and rejects all negative ones, as a constraint satisfaction problem, and then propose a parallel algorithm to solve the problem. The results of computational experiments on the variety of test samples are reported.

关键词： parallel algorithm learning regular languages using nondeterministic finite automata constraint satisfaction and satisfiability problems grammatical inference

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A parallel algorithm for ridge-penalized estimation of the multivariate exponential family from data of mixed types

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STATISTICS AND COMPUTING 2021年第4期31卷 41-41页

作者： Trip, Diederik S. Laman van Wieringen, Wessel N. Delft Univ Technol Kavli Inst Nanosci Dept Bionanosci Maasweg 9 NL-2629 HZ Delft Netherlands Amsterdam UMC Dept Epidemiol & Data Sci Amsterdam Publ Hlth Res Inst Locat VUmc POB 7057 NL-1007 MB Amsterdam Netherlands Vrije Univ Amsterdam Dept Math De Boelelaan 1081a NL-1081 HV Amsterdam Netherlands

Computationally efficient evaluation of penalized estimators of multivariate exponential family distributions is sought. These distributions encompass among others Markov random fields with variates of mixed type (e.g., binary and continuous) as special case of interest. The model parameter is estimated by maximization of the pseudo-likelihood augmented with a convex penalty. The estimator is shown to be consistent. With a world of multi-core computers in mind, a computationally efficient parallel Newton-Raphson algorithm is presented for numerical evaluation of the estimator alongside conditions for its convergence. parallelization comprises the division of the parameter vector into subvectors that are estimated simultaneously and subsequently aggregated to form an estimate of the original parameter. This approach may also enable efficient numerical evaluation of other high-dimensional estimators. The performance of the proposed estimator and algorithm are evaluated and compared in a simulation study. Finally, the presented methodology is applied to data of an integrative omics study.

关键词： Markov random field Consistency Pseudo-likelihood Block-wise Newton– Raphson Network parallel algorithm Graphical model

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An efficient parallel algorithm for DNS of buoyancy-driven turbulent flows

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Journal of Hydrodynamics 2019年第6期31卷 1159-1169页

作者： Yi-zhao Zhang Shu-ning Xia Yu-hong Dong Bo-fu Wang Quan Zhou Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Institute of Applied Mathematics and MechanicsSchool of Mechanics and Engineering ScienceShanghai UniversityShanghai 200072China

In this paper,an explicit low-storage simplified M-stage Runge-Kutta(SRK)scheme for high Reynolds-number incompressible flows is *** the SRK scheme,the Poisson equation is solved only once in the final substage of each time *** taking advantage of the SRK scheme and the advanced hybrid MPI+MPI model,we have developed an efficient parallel solver for buoyancy-driven turbulent *** spatial and temporal accuracies of the solver are validated with Taylor-Green vortex *** the RK and SRK schemes are implemented for the simulation of turbulent Rayleigh-Benard convection as well as Rayleigh-Taylor *** results show that the SRK scheme can save approximately 20%of the computation time.

关键词： Direct numerical simulation(DNS) parallel algorithm turbulent flows

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A new constant-time parallel algorithm for merging

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JOURNAL OF SUPERCOMPUTING 2019年第2期75卷 968-983页

作者： Bahig, Hazem M. Ain Shams Univ Fac Sci Dept Math Div Comp Sci Cairo 11566 Egypt Hail Univ Comp Sci & Engn Coll Hail Saudi Arabia

Merging is the process of constructing a sorted array C from two sorted arrays A and B of lengths nA and nB, respectively, such that array C contains the elements of arrays A and B. The problem is a fundamental subroutine in many applications such as tree reconstruction, database design, and sorting. In this paper, we present a constant-time integer merging algorithm on a shared memory model with a concurrent read operation. No constant-time algorithm for integer merging on this model was designed previously. The algorithm, which is based on cross-rank and address strategy, works when the elements of the inputs belong to the integer domain. The presented algorithm has the following advantages:- (1) running in constant time;(2) stable;(3) simple;(4) optimal when the domain of integers is less than or equal to the size of the inputs and the number of processors is equal to size of the inputs;and (5) deterministic.

关键词： Integer merging parallel algorithm Shared memory Concurrent read Constant-time algorithm

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A parallel algorithm for Mining Non-Redundant Recurrent Rules from a Sequence Database

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2019年第5期23卷 956-961页

作者： Yoon, Seung-Yong Seki, Hirohisa Nagoya Inst Technol Dept Comp Sci Showa Ku Gokiso Cho Nagoya Aichi 4668555 Japan

We propose a parallel algorithm for mining non-redundant recurrent rules from a sequence database. Recurrent rules, proposed by Lo et al. [1], can express "Whenever a series of precedent events occurs, eventually a series of consequent events occurs," and they have shown the usefulness of recurrent rules in various domains, including software specification and verification. Although some algorithms such as NR3 have been proposed, mining non-redundant recurrent rules still requires considerable processing time. To reduce the computation cost, we present a parallel approach to mining non-redundant recurrent rules, which fully utilizes the task-parallelism in NR3. We also give some experimental results, which show the effectiveness of our proposed method.

关键词： data mining sequential rule recurrent rule sequence database parallel algorithm

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