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A Thread-Oriented Memory Resource Management Framework for Mobile Edge Computing

IEEE ACCESS 2019年 7卷 45881-45890页

作者： Zhu, Zongwei Wu, Fan Cao, Jing Li, Xi Jia, Gangyong Univ Sci & Technol China Suzhou Inst Adv Study Suzhou 215000 Peoples R China Hangzhou Dianzi Univ Dept Comp Sci Hangzhou 310000 Zhejiang Peoples R China

Increasing the number of cores is one of the most effective methods to enhance performance. However, an extensive experimental study on mobile edge computing (e.g., Android devices) indicates that the memory management system has gradually become a key performance bottleneck. Studies on improving memory management mainly focus on exploring the trade-off between avoiding fragmentation and improving allocation efficiency. From our previous research, we know that the fragmentation is no longer a crucial bottleneck;instead, inter- and intra-thread behavior should be focused on, and thus, we introduce memory management based on thread behaviors (MMBTB). Unfortunately, it lacks a unified optimization program interface and good architecture. Consequently, in this paper, we propose a memory resource management at operating system (OS) layer of mobile edge computing, called the thread-oriented memory management layer (TOMML) to address this problem, which follows the microkernel architecture pattern and can meet the user's requirements for selecting plug-ins to achieve different optimization goals. This paper is divided into several sections as follows. First, we demonstrate the efficiency of TOMML through theoretical simulation and experimentation. The experimental result is that TOMML can improve memory allocation efficiency by 12%-20%. Furthermore, we introduce a plug-in to save power, which can further promote 6%-25% bank free compared with previous excellent research.

关键词： Memory management power control parallel algorithms edge computing thread behaviors

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algorithms to compute the Burrows-Wheeler Similarity Distribution

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THEORETICAL COMPUTER SCIENCE 2019年第0期782卷 145-156页

作者： Louza, Felipe A. Telles, Guilherme P. Gog, Simon Zhao, Liang Univ Sao Paulo Dept Comp & Math Ribeirao Preto Brazil Univ Estadual Campinas Inst Comp Campinas SP Brazil eBay Inc San Jose CA USA

The Burrows-Wheeler transform (BWT) is a well studied text transformation widely used in data compression and text indexing. The BWT of two strings can also provide similarity measures between them, based on the observation that the more their symbols are intermixed in the transformation, the more the strings are similar. In this article we present two new algorithms to compute similarity measures based on the BWT for string collections. In particular, we present practical and theoretical improvements to the computation of the Burrows-Wheeler Similarity Distribution for all pairs of strings in a collection. Our algorithms take advantage of the BWT computed for the concatenation of all strings, and use compressed data structures that allow reducing the running time with a small memory footprint, as shown by a set of experiments with real and artificial datasets. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Burrows-Wheeler transform String similarity String collections Compressed data structures parallel algorithms

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Efficient parallel algorithm for mining high utility patterns based on spark 4

Efficient parallel algorithm for mining high utility pattern...

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4th IEEE International Conference on Data Science in Cyberspace, DSC 2019

作者： Liu, Junqiang Zhao, Rong Yang, Xiangcai Zhang, Yong Jiang, Xiaoning School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China

ISBN: (纸本)9781728145280

Utility pattern mining is an important data mining technology that can find patterns that are both statistically significant and in accordance with users' expectations and objectives, which emerged recently to address the limitation of frequent pattern mining. To the best of our knowledge, utility pattern mining algorithms proposed so far however are all sequential ones that are inefficient and not scalable when dealing with huge amounts of data. To address the scalability and efficiency challenge, this paper proposes a parallel utility pattern mining algorithm based on Spark, a parallel programming model that uses in-memory storage for data sharing. The contributions include an improved vertical data structure, a three-phase parallel mining framework, and an efficient algorithm. Extensive experiments on both artificial and real-world data show that the proposed parallel algorithm is scalable and efficient. © 2019 IEEE.

关键词： parallel algorithms

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Fast and efficient contrast-enhanced super-resolution without real-world data using concatenated recursive compressor-decompressor network

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IET IMAGE PROCESSING 2019年第5期13卷 817-824页

作者： Choi, Jun-Myung Kang, Dong-Joong Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea

The authors propose a novel model called concatenated recursive compressor-decompressor network (CRCDNet) for contrast-enhanced super-resolution. The characteristics of authors' model can be summarised as follows. First, a compression-decompression process reduces the computational complexity compared with the general fully convolutional model. Second, an internal/external skip-connection is used to preserve information of the preceding layers. Finally, by employing a recursive module, authors' model has a small number of parameters, yet is a deep and robust network. The authors apply authors' proposed network to license plate images. As a real application, license plates can provide important evidence for investigation of crimes and for security, but it is very difficult to collect the vast amounts of license plates required for analysis based on a data-driven approach. To solve this problem, the authors generated virtual datasets to train authors' model, while analysing the performance with real license plate datasets. Authors' method achieves better performance than the state-of-the-art models on license plate images.

关键词： learning (artificial intelligence) image sensors traffic engineering computing parallel algorithms image resolution object recognition data compression image enhancement computational complexity license plate datasets state-of-the-art models license plate images fast contrast-enhanced super-resolution efficient contrast-enhanced super-resolution real-world data concatenated recursive compressor-decompressor network compression-decompression process general fully convolutional model recursive module deep network robust network license plates internal-external skip-connection CRCDNet

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An Insight into parallel Computing Paradigm 2

An Insight into Parallel Computing Paradigm

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2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2019

作者： Dash, Yajnaseni Indian Institute of Technology Delhi New Delhi India

ISBN: (纸本)9781728102832

This millennium will observe the rising growth of parallel computing and wider use of these technologies at every level of mainstream computing. parallel computing is becoming an essential computational tool used for saving time and cost complexity, doing several things all at once, solving complicated problems within a short span of time, making better utilization of parallel hardware and overcoming the memory constraint. This scientific area of research will progress from a niche focusing on finding solutions to scientific and engineering problems to become one of the significant computing technologies. This study provides an overview of types of parallelism, characteristics, steps, approaches of parallel algorithm, and different applications of parallel computing technologies which will be beneficial for researchers for further exploration into this emerging field of research. © 2019 IEEE.

关键词： parallel algorithms

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ParaKavosh: A parallel Algorithm for Finding Biological Network Motifs

ParaKavosh: A Parallel Algorithm for Finding Biological Netw...

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Conference on Information and Knowledge Technology

作者： Zahra Razaghi Moghadam Kashani Ali Masoudi-Nejad Abbas Nowzari-Dalini Systems Biology and Mathematical Modelling Group Max Planck Institute of Molecular Plant Physiology Potsdam Germany Laboratory of Systems Biology Bioinformatics Institute of Biochemistry and Biophysic University of Tehran Tehran Iran School of Mathematics and Computer Science College of Science University of Tehran Tehran Iran

ISBN: (数字)9781665404419

ISBN: (纸本)9781665404426

Biological networks have recently gathered much attraction in finding their motifs. Motifs can be considered as subgraphs that occur in a particular network at significantly higher frequencies than random networks. The importance of this problem causes attention of improving the existing algorithms. As the runtime of an algorithm is an important aspect, applying parallel techniques is appropriate for better improvement. In this paper a parallel algorithm (ParaKavosh) for finding network motifs is presented. Our algorithm is tested on E. coli, S. cerevisiae, Homo sapiens and Rattus norvegicus networks. The cost optimality of the algorithm is also shown by analyzing the obtained results with an efficient sequential algorithm. The results show that the algorithm performs much better in terms of runtime.

关键词： Knowledge engineering Runtime Biology parallel algorithms Faces

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

arXiv

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arXiv 2020年

作者： White, Larz Bell, W. Bryan Haygood, Ryan Lockheed Martin Aeronautics Fort WorthTX76108 United States

Computed Tomographic Imaging Spectrometers (CTIS) capture hyperspectral images in realtime. However, post processing the imagery can require enormous computational resources;thus, limiting its application to non-realtime scenarios. To overcome these challenges we developed a highly parallelizable algorithm that exploits spatial shift-invariance. To demonstrate the versatility of our new algorithm, we developed implementations on a desktop and an embedded graphics processing unit (GPU). To our knowledge, our results show the fastest image reconstruction times reported. Copyright © 2020, The Authors. All rights reserved.

关键词： parallel algorithms

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parallel Programming of Churn Prediction Using Gaussian Naïve Bayes

Parallel Programming of Churn Prediction Using Gaussian Naï...

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International Conference of Information and Communication Technology (ICoICT)

作者： Dandi T. Barus R. Elfarizy F. Masri P. H. Gunawan School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9781728161426

ISBN: (纸本)9781728161433

This paper presents churn predictions with the Gaussian Naïve Bayes method. Churn prediction is a forecasting method to predict customer decisions in a company's service or product (churn). With high public enthusiasm and an increasing number of customers in the Big Data era, a fast computing process is needed to predict churn as quickly as possible. In this paper, computing is accelerated by the OpenMP platform parallel algorithm. Churn prediction experiments are performed with different amounts of test data, ranging from 100, 300, 500, 700, to 900 data. The results obtained show that implementing OpenMP in predicting churn is faster than serial processing. The obtained speedup and efficiency reached more than 1.49 and 37%, even for test data of 300 and 500, based on the tests, the speedup and efficiency reached 1.99 and 50%.

关键词： Prediction algorithms parallel algorithms Feature extraction Probability Mathematical model Information and communication technology

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A parallel Algorithm Based on OpenMP for Human Brain Model of Transcranial Magnetic Stimulation

A Parallel Algorithm Based on OpenMP for Human Brain Model o...

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Peixian Zhang Hongbin Wang Shuai Zhang State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

Aiming at the problems of complex and variable bio-electromagnetic computing, large amount of calculation, and insufficient calculation accuracy to meet the actual clinical needs, a parallel algorithm based on OpenMP was introduced. A multi-threaded operation of the electromagnetic computing model was realized by using a mixed programming method. The parallel method work at a single-computer with multi-core is performed on the electromagnetic calculation process of the human brain model stimulated by the transcranial magnetic stimulation coil, which improves the calculation efficiency of the model, so that the electromagnetic calculation of the brain model with higher accuracy can be satisfied.

关键词： transcranial magnetic stimulation human brain model parallel computing Transcranial Magnetic Stimulation Brain modeling parallel algorithms parallel PROCESSING (COMPUTERS) computational procedure computation accuracy ELECTROMAGNETIC

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parallel FDFD Algorithm Based on MPI and Its Application

Parallel FDFD Algorithm Based on MPI and Its Application

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Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC)

作者： Xiang-Lin Li Bing Wei Xin-Bo He Zhi-Wei Gao School of Physics and Optoelectronic Engineering Xidian University Xi'an Shaanxi China School of Computer and Information Engineering Shijiazhuang TieDao University Shijiazhuang Hebei China

ISBN: (数字)9781728181813

ISBN: (纸本)9781728181820

In order to solve the problem of slow computation speed and difficulty in solving electromagnetic response of electrically large targets by using finite difference frequency domain (FDFD) serial method, a parallel FDFD calculation method based on MPI memory sharing mechanism is proposed. In the method, the complex sparse matrix is allocated to each sub process by row, and the intermediate matrix used in all processes is stored in the shared memory to reduce the memory consumption. The conjugate gradient method is used to obtain the solution of the equation. Numerical results show the correctness and efficiency of the proposed method.

关键词： Wireless communication Memory management Software algorithms Scattering Software Sparse matrices parallel algorithms

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