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11th International Conference on Information and Knowledge Technology, IKT 2020

作者： Kashani, Zahra Razaghi Moghadam Masoudi-Nejad, Ali Nowzari-Dalini, Abbas Systems Biology and Mathematical Modelling Group Max Planck Institute of Molecular Plant Physiology Potsdam14476 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: (纸本)9781665404419

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. © 2020 IEEE.

关键词： parallel algorithms

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Study of Fine-Grained, Irregular parallel Applications on a Many-Core Processor

Study of Fine-Grained, Irregular Parallel Applications on a ...

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作者： Edwards, James Alexander University of Maryland College Park

学位级别：Ph.D.

This dissertation demonstrates the possibility of obtaining strong speedups for a variety of parallel applications versus the best serial and parallel implementations on commodity platforms. These results were obtained using the PRAM-inspired Explicit Multi-Threading (XMT) many-core computing platform, which is designed to efficiently support execution of both serial and parallel code and switching between the two. Biconnectivity: For finding the biconnected components of a graph, we demonstrate speedups of 9x to 33x on XMT relative to the best serial algorithm using a relatively modest silicon budget. Further evidence suggests that speedups of 21x to 48x are possible. For graph connectivity, we demonstrate that XMT outperforms two contemporary NVIDIA GPUs of similar or greater silicon area. Prior studies of parallel biconnectivity algorithms achieved at most a 4x speedup, but we could not find biconnectivity code for GPUs to compare biconnectivity against them. Triconnectivity: We present a parallel solution to the problem of determining the triconnected components of an undirected graph. We obtain significant speedups on XMT over the only published optimal (linear-time) serial implementation of a triconnected components algorithm running on a modern CPU. To our knowledge, no other parallel implementation of a triconnected components algorithm has been published for any platform. Burrows-Wheeler compression: We present novel work-optimal parallel algorithms for Burrows-Wheeler compression and decompression of strings over a constant alphabet and their empirical evaluation. To validate these theoretical algorithms, we implement them on XMT and show speedups of up to 25x for compression, and 13x for decompression, versus bzip2, the de facto standard implementation of Burrows-Wheeler compression. Fast Fourier transform (FFT): Using FFT as an example, we examine the impact that adoption of some enabling technologies, including silicon photonics, would have on the perfo

关键词： Computer engineering Computer science manycore processor parallel algorithms PRAM XMT

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Gpu-accelerated parallel aligning long reads with high error rate using enhanced sparse suffix array 10th

Gpu-accelerated parallel aligning long reads with high error...

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10th International Symposium on parallel Architectures, algorithms and Programming, PAAP 2019

作者： Wei, Hao Zhong, Cheng Chen, Danyang Yin, Mengxiao Zhang, Jinxiong School of Computer Electronics and Information Guangxi University Nanning Guangxi530004 China

The read alignment (sequence alignment) is one of the most basic and time-consuming problems in Bioinformatics. In this paper, a CPU-GPU parallel long-read alignment method is studied to solve this problem. A lightweight data structure using enhanced sparse suffix array is used to store the index of reference genome in order to adapt to the limited memory capacity of GPU architecture. The two-dimensional search space between the reference genome and long reads is divided into several search sub-spaces. The massive long reads alignment is further divided into the multiple long-read alignments with smaller size. A CPU-GPU parallel algorithm aligning long reads with high error rate is implemented by improving the seeds selection scheme. The experimental results show that the parallel algorithm can accelerate remarkably the long-read alignment while maintaining the alignment accuracy and recall rate as a whole. © Springer Nature Singapore Pte Ltd. 2020.

关键词： parallel algorithms

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Using Floating-Point Intervals for Non-Modular Computations in Residue Number System

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IEEE ACCESS 2020年 8卷 58603-58619页

作者： Isupov, Konstantin Vyatka State Univ Dept Elect Comp Machines Kirov 610000 Russia

The residue number system (RNS) provides parallel, carry-free, and high-speed arithmetic and is therefore a good tool for high-performance computing. However, operations such as magnitude comparison, sign computation, overflow detection, scaling, and division are difficult to perform in RNS, since it is problematic to determine the magnitude of an RNS number. In order to resolve this problem, we propose to compute the interval evaluation of the fractional representation of an RNS number in floating-point arithmetic of limited precision. No matter what the size of the moduli set and dynamic range, only small arithmetic operations are required, and most of the computations are performed in parallel with threads, which allows for efficient implementation of our method on many general-purpose computing platforms. Using this method, we propose new algorithms for magnitude comparison and general division in RNS and implement them for GPUs using the CUDA platform. We evaluate the performance of our algorithms on an NVIDIA GTX 1080 GPU using sets of 4 to 256 RNS moduli that provide dynamic ranges from 64 to 4096 bits. Experimental results show that the proposed new algorithms are efficient for large moduli sets and clearly outperform the existing RNS magnitude comparison and division algorithms in terms of execution time.

关键词： Heuristic algorithms Cathode ray tubes Dynamic range Signal processing algorithms Graphics processing units Memory management Cryptography Residue number system floating-point arithmetic non-modular operations magnitude comparison division high performance parallel algorithms graphics processing unit CUDA

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Improving the Efficiency of SVM Classification With FHE

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2020年 15卷 1709-1722页

作者： Bajard, Jean-Claude Martins, Paulo Sousa, Leonel Zucca, Vincent Sorbonne Univ Lab Informat Paris 6 CNRS F-75006 Paris France Univ Lisbon Inst Super Tecn INESC ID P-1649004 Lisbon Portugal Univ Wollongong Inst Cybersecur & Cryptol Wollongong NSW 2522 Australia

In an ever more data-centric economy, machine learning models have risen in importance. With the large amounts of data companies collect, they are able to develop highly accurate models to predict the behaviours of their customers. It is thus important to safeguard the data used to build these models to prevent competitors from mimicking their services. In addition, as this type of techniques finds its way into areas that need to deal with more sensitive information, like the medical industry, the privacy of the data that needs to be classified also has to be ensured. Herein, this topic is addressed by homomorphically evaluating Support Vector Machine (SVM) models, in a way that guarantees that a client learns nothing about the model except for the classification of his data, and that the service provider learns nothing about the data. Whereas, previously, Fully Homomorphic Encryption (FHE) has mostly focused on either bit-wise or value-wise computations, SVMs present an additional challenge since they combine both: during an initial phase a kernel function is evaluated that makes use of real arithmetic, and during a second phase the sign bit has to be extracted. Novel techniques are herein proposed that allow for speedups of up to 2.7 and 6.6 for the evaluation of polynomials and the determination of sign, respectively, in comparison to the state of the art. Finally, it is shown that the proposed techniques do not deteriorate the classification accuracy of the SVM models.

关键词： Support vector machines Kernel Cryptography Biological system modeling Data models Companies Computational modeling Support vector machine homomorphic encryption computer arithmetic parallel algorithms

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Multi-round cooperative search games with multiple players

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2020年 113卷 125-149页

作者： Korman, Amos Rodeh, Yoav CNRS Paris France Univ Paris Diderot Paris France Ort Braude Coll Karmiel Israel

A treasure is placed in one of M boxes according to a known distribution and k searchers are searching for it in parallel during T rounds. How can one incentivize selfish players so that the probability that at least one player finds the treasure is maximized? We focus on congestion policies C(l) specifying the reward a player receives being one of the l players that (simultaneously) find the treasure first. We prove that the exclusive policy, in which C(1) = 1 and C(l) = 0 for l > 1, yields a price of anarchy of (1 -(1 - 1/k)(k))(-1), which is the best among all symmetric reward policies. We advocate the use of symmetric equilibria, and show that besides being fair, they are highly robust to crashes of players. Indeed, in many cases, if some small fraction of players crash, symmetric equilibria remain efficient in terms of their group performance while also serving as approximate equilibria. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Algorithmic mechanism design parallel algorithms Collaborative search Fault-tolerance Price of anarchy Price of stability Symmetric equilibria

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Where did the tumor start? An inverse solver with sparse localization for tumor growth models

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INVERSE PROBLEMS 2020年第4期36卷 045006页

作者： Subramanian, Shashank Scheufele, Klaudius Mehl, Miriam Biros, George Univ Texas Austin Oden Inst Computat Engn & Sci 201 E 24th St Austin TX 78712 USA Univ Stuttgart Inst Parallel & Distributed Syst Univ Str 38 Stuttgart Germany

We present a numerical scheme for solving an inverse problem for parameter estimation in tumor growth models for glioblastomas, a form of aggressive primary brain tumor. The growth model is a reaction-diffusion partial differential equation (PDE) for the tumor concentration. We use a PDE-constrained optimization formulation for the inverse problem. The unknown parameters are the reaction coefficient (proliferation), the diffusion coefficient (infiltration), and the initial condition field for the tumor PDE. Segmentation of magnetic resonance imaging (MRI) scans drive the inverse problem where segmented tumor regions serve as partial observations of the tumor concentration. Like most cases in clinical practice, we use data from a single time snapshot. Moreover, the precise time relative to the initiation of the tumor is unknown, which poses an additional difficulty for inversion. We perform a frozen-coefficient spectral analysis and show that the inverse problem is severely ill-posed. We introduce a biophysically motivated regularization on the structure and magnitude of the tumor initial condition. In particular, we assume that the tumor starts at a few locations (enforced with a sparsity constraint on the initial condition of the tumor) and that the initial condition magnitude in the maximum norm is equal to one. We solve the resulting optimization problem using an inexact quasi-Newton method combined with a compressive sampling algorithm for the sparsity constraint. Our implementation uses PETSc and AccFFT libraries. We conduct numerical experiments on synthetic and clinical images to highlight the improved performance of our solver over a previously existing solver that uses standard two-norm regularization for the calibration parameters. The existing solver is unable to localize the initial condition. Our new solver can localize the initial condition and recover infiltration and proliferation. In clinical datasets (for which the ground truth is unknown), our sol

关键词： brain tumor growth models PDE constrained optimization compressive sampling parallel algorithms

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Energy-Aware Application Placement in Mobile Edge Computing: A Stochastic Optimization Approach

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2020年第4期31卷 909-922页

作者： Badri, Hossein Bahreini, Tayebeh Grosu, Daniel Yang, Kai Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

The Quality of Service (QoS) in Mobile Edge Computing (MEC) systems is significantly dependent on the application offloading and placement decisions. Due to the movement of users in MEC networks, an optimal application placement might turn into the least efficient placement in few minutes. Thus, it is crucial to take the dynamics of the system into account when designing application placement mechanisms. On the other hand, energy consumption of servers is a significant component of the cost of services in MEC systems and must also be considered in the design of the mechanisms. In this article, we model the problem of energy-aware application placement in edge computing systems as a multi-stage stochastic program. The objective is to maximize the QoS of the system while taking into account the limited energy budget of the edge servers. To solve the problem, we design a novel parallel Sample Average Approximation (SAA) algorithm. We conduct an extensive experimental analysis to evaluate the performance of the proposed algorithm using real-world trace data.

关键词： Mobile edge computing energy-aware application placement quality of service multi-stage stochastic programming sample average approximation parallel algorithms

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ConcurrentHull: A Fast parallel Computing Approach to the Convex Hull Problem 15th

ConcurrentHull: A Fast Parallel Computing Approach to the Co...

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15th International Symposium on Visual Computing, ISVC 2020

作者： Masnadi, Sina LaViola, Joseph J. University of Central Florida OrlandoFL United States

ISBN: (纸本)9783030645557

The convex hull problem has practical applications in mesh generation, file searching, cluster analysis, collision detection, image processing, statistics, etc. In this paper, we present a novel pruning-based approach for finding the convex hull set for 2D and 3D datasets using parallel algorithms. This approach, which is a combination of pruning, divide and conquer, and parallel computing, is flexible to be employed in a distributed computing environment. We propose the algorithm for both CPU and GPU (CUDA) computation models. The results show that ConcurrentHull has a performance gain as the input data size increases. Providing an independently dividable approach, our algorithm has the benefit of handling huge datasets as opposed to other approaches presented in this paper which failed to manage the same datasets. © 2020, Springer Nature Switzerland AG.

关键词： parallel algorithms

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Optimization for Multi-Join Queries on the GPU

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IEEE ACCESS 2020年 8卷 118380-118395页

作者： Hu, Xue-Xuan Xi, Jian-Qing Tang, De-You South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China

Multi-join queries are important operations in data management systems and data integration systems, and their efficiency has attracted the attention of researchers. In recent years, graphics processing units (GPUs) have developed rapidly and become a powerful tool for parallel computing, providing a new idea for multi-join query optimization. This paper studies the use of GPU technology to optimize multi-join queries and focuses on two points: 1) a multi-phase optimization strategy and 2) optimization methods of each stage. For the first point, we discuss a two-phase optimization strategy on the GPU and prove the effectiveness of this strategy. For the second point, we provide an establishment method of a minimum cost join tree on the GPU, the parallel execution methods of intra-join and inter-join on the GPU, and a strategy of scheduling multiple joins to execute in parallel on the GPU. Experimental results show that the multi-join query optimization proposed in this paper improves the efficiency of multi-join queries, especially in the case of high load and complex join queries, achieving higher throughput than that of previous optimization algorithms.

关键词： Graphics processing units Heuristic algorithms parallel algorithms Optimization methods Estimation GPU multi-join query parallel optimization two-phase optimization strategy

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