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检索条件"主题词=Parallel algorithm"

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Efficient for RCS computation of long and complex dielectric objects

Efficient for RCS computation of long and complex dielectric...

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36th European Microwave Conference

作者： Perez, Saul Fares, M'Barek Souny, Bernard ENAC 7 Ave Edouard Belin BP 4005 F-31055 Toulouse France CERFACS F-31057 Toulouse France ENAC F-31055 Toulouse France

ISBN: (纸本)9782960055160

We propose a new method for RCS computation of long internally complex dielectric objects such as wind turbine blades. The method proposed here is an improved iterative algorithm whose convergence is proven. Thanks to the geometrical decomposition technique, three accelerating methods can be used efficiently In an iterative algorithm. The first accelerating method allows the elimination of the internal degrees of freedom with minimal cost. The second accelerating method (matrix compression QR) accelerates all the matrix vector products used in the preconditioning procedure as well as in the GMRES iterative resolution. The third accelerating method consists in using a "geometric neighboring" preconditioner adapted to the physical aspect of the problem.

关键词： integral equations method of moment parallel algorithm iterative methods

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GPU-based PQGA algorithm for Estimating Parameters of Muskingum Model 13

GPU-based PQGA Algorithm for Estimating Parameters of Muskin...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Ouyang, Aijia Luerssen, Martin H. Wang, Qian Peng, Xuyu Zunyi Normal Coll Dept Informat Engn Zunyi 563006 Guizhou Peoples R China Flinders Univ S Australia Sch Comp Sci Engn & Math Adelaide SA 5001 Australia Zunyi Normal Coll Dept Comp Sci Zunyi 563006 Guizhou Peoples R China

ISBN: (纸本)9781538621653

Accurate parameter estimation for the Muskingum method is important in its use for forecasting flood damage. Combining ideas from quantum computing and evolutionary computing, a quantum genetic algorithm (QGA) is presented for this purpose, which is shown to offer higher precision and greater robustness than eight typical methods. However, it requires longer computation time. In this paper, we therefore provide an implementation of a parallel quantum genetic algorithm (PQGA) in C-CUDA for estimating the parameters of the Muskingum model. PQGA was tested on a classic river example from China. Experimental results show that the PQGA scales well with available computing resources and is substantially faster than QGA and the other methods.

关键词： Quantum computing genetic algorithm GPU parallel algorithm Muskingum model

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Accelerating Sequence Alignment to Graphs 33

Accelerating Sequence Alignment to Graphs

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33rd IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Jain, Chirag Misra, Sanchit Zhang, Haowen Dilthey, Alexander Aluru, Srinivas Georgia Inst Technol Sch Computat Sci & Engr Atlanta GA 30332 USA Intel Corp Parallel Comp Lab Bangalore Karnataka India Univ Hosp Dusseldorf Inst Med Microbiol Dusseldorf Germany

ISBN: (纸本)9781728112466

Aligning DNA sequences to an annotated reference is a key step for genotyping in biology. Recent scientific studies have demonstrated improved inference by aligning reads to a variation graph, i.e., a reference sequence augmented with known genetic variations. Given a variation graph in the form of a directed acyclic string graph, the sequence to graph alignment problem seeks to find the best matching path in the graph for an input query sequence. Solving this problem exactly using a sequential dynamic programming algorithm takes quadratic time in terms of the graph size and query length, making it difficult to scale to high throughput DNA sequencing data. In this work, we propose the first parallel algorithm for computing sequence to graph alignments that leverages multiple cores and single-instruction multiple-data (SIMD) operations. We take advantage of the available inter-task parallelism, and provide a novel blocked approach to compute the score matrix while ensuring high memory locality. Using a 48-core Intel Xeon Skylake processor, the proposed algorithm achieves peak performance of 317 billion cell updates per second (GCUPS), and demonstrates near linear weak and strong scaling on up to 48 cores. It delivers significant performance gains compared to existing algorithms, and results in run-time reduction from multiple days to three hours for the problem of optimally aligning high coverage long (PacBio/ONT) or short (Illumina) DNA reads to an MHC human variation graph containing 10 million vertices.

关键词： parallel algorithm architecture-aware optimization variation graphs sequence alignment genomics

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STOCHASTIC FORWARD-BACKWARD AND PRIMAL-DUAL APPROXIMATION algorithmS WITH APPLICATION TO ONLINE IMAGE RESTORATION 24

STOCHASTIC FORWARD-BACKWARD AND PRIMAL-DUAL APPROXIMATION AL...

引用

24th European Signal Processing Conference (EUSIPCO)

作者： Combettes, Patrick L. Pesquet, Jean-Christophe UPMC Univ Paris 06 Sorbonne Univ Lab Jacques Louis Lions UMR 7598 Paris France Univ Paris Est Lab Informat Gaspard Monge CNRS UMR 8049 Champs Sur Marne France

ISBN: (纸本)9780992862657

Stochastic approximation techniques have been used in various contexts in data science. We propose a stochastic version of the forward-backward algorithm for minimizing the sum of two convex functions, one of which is not necessarily smooth. Our framework can handle stochastic approximations of the gradient of the smooth function and allows for stochastic errors in the evaluation of the proximity operator of the nonsmooth function. The almost sure convergence of the iterates generated by the algorithm to a minimizer is established under relatively mild assumptions. We also propose a stochastic version of a popular primal-dual proximal splitting algorithm, establish its convergence, and apply it to an online image restoration problem.

关键词： convex optimization nonsmooth optimization primal-dual algorithm stochastic algorithm parallel algorithm proximity operator recovery image restoration

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A new cellular automata approach to prediction coding

A new cellular automata approach to prediction coding

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20th International Conference on Computers and Their Applications

作者： Shuai, DX Feng, X E China Univ Sci & Technol Dept Comp Sci Shanghai 200237 Peoples R China

ISBN: (纸本)1880843544

This paper presents a new one-dimensional cellular automata (CA) approach to parallelly generate the prediction coding for lossless data compression. The local functions of CA, parallel algorithm and its implementation structure to generate the desired predictive coding are discussed. The properties of the proposed CA, including the convergency' complexity and correctness are given. The proposed CA approach has advantages in terms of encoding speed, parallelism, scalability, simplicity, and easy hardware implementation over other sequential lossless compression methods.

关键词： cellular automata prediction coding lossless data compression parallel algorithm local functions

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HAGP: A Hub-centric Asynchronous Graph Processing Framework for Scale-free Graph 15

HAGP: A Hub-centric Asynchronous Graph Processing Framework ...

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2015 15th IEEE ACM International Symposium on Cluster Cloud and Grid Computing (CCGrid 2015)

作者： Gao, Tao Lu, Yutong Zhang, Baida Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781479980062

Graph structure which is often used to model the relationship between the data items has drawn more and more attention. The graph datasets from many important domains have the property called scale-free. In the scale-free graphs, there exist the hubs, which have much larger degree than the average value. The hubs may cause the problems of load imbalance, poor scalability and high communication overhead when the graphs are processed in the distributed memory systems. In this paper, we design an asynchronous graph processing framework targeted for distributed memory by considering the hubs as a separate part of the vertexes, which we call it the hub-centric idea. Specifically speaking, a hub-duplicate graph partitioning method is proposed to balance the workload and reduce the communication overhead. At the same time, an efficient asynchronous state synchronization method for the duplicates is also proposed. In addition, a priority scheduling strategy is applied to further reduce the communication overhead.

关键词： graph processing graph algorithm parallel algorithm distributed computing

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A Scalable Numerical algorithm for Solving Tikhonov Regularization Problems 11th

A Scalable Numerical Algorithm for Solving Tikhonov Regulari...

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11th International Conference on parallel Processing and Applied Mathematics (PPAM)

作者： Arcucci, Rosella D'Amore, Luisa Celestino, Simone Laccetti, Giuliano Murli, Almerico Univ Naples Federico II Naples Italy Euro Mediterranean Ctr Climate Change CMCC Lecce Italy SPACI Naples Italy Imperial Coll London London England

ISBN: (纸本)9783319321523;9783319321516

We present a numerical algorithm for solving large scale Tikhonov Regularization problems. The approach we consider introduces a splitting of the regularization functional which uses a domain decomposition, a partitioning of the solution and modified regularization functionals on each sub domain. We perform a feasibility analysis in terms of the algorithm and software scalability, to this end we use the scale-up factor which measures the performance gain in terms of time complexity reduction. We verify the reliability of the approach on a consistent test case (the Data Assimilation problem for oceanographic models).

关键词： Tikhonov regularization Large scale inverse problems parallel algorithm Data assimilation

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Bulk GCD Computation Using a GPU to Break Weak RSA Keys 29

Bulk GCD Computation Using a GPU to Break Weak RSA Keys

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29th IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Fujita, Toru Nakano, Koji Ito, Yasuaki Hiroshima Univ Dept Informat Engn Kagamiyama 1-4-1 Higashihiroshima 7398527 Japan

ISBN: (纸本)9781467376846

RSA is one the most well-known public-key cryptosystems widely used for secure data transfer. An RSA encryption key includes a modulus n which is the product of two large prime numbers p and q. If an RSA modulus n can be decomposed into p and q, the corresponding decryption key can be computed easily from them and the original message can be obtained using it. RSA cryptosystem relies on the hardness of factorization of RSA modulus. Suppose that we have a lot of encryption keys collected from the Web. If some of them are inappropriately generated so that they share the same prime number, then they can be decomposed by computing their GCD (Greatest Common Divisor). Actually, a previously published investigation showed that a certain ratio of RSA moduli in encryption keys in the Web are sharing prime numbers. We may find such weak RSA moduli n by computing the GCD of many pairs of RSA moduli. The main contribution of this paper is to present a new Euclidean algorithm for computing the GCD of all pairs of encryption moduli. The idea of our new Euclidean algorithm that we call Approximate Euclidean algorithm is to compute an approximation of quotient by just one 64-bit division and to use it for reducing the number of iterations of the Euclidean algorithm. We also present an implementation of Approximate Euclidean algorithm optimized for CUDA-enabled GPUs. The experimental results show that our implementation for 1024-bit GCD on GeForce GTX 780Ti runs more than 80 times faster than the Intel Xeon CPU implementation. Further, our GPU implementation is more than 9 times faster than the best known published GCD computation using the same generation GPU.

关键词： CUDA Euclidean algorithm GPU parallel algorithm

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Run-Based Connected Components Labeling Using Double-Row Scan 9th

Run-Based Connected Components Labeling Using Double-Row Sca...

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9th International Conference on Image and Graphics (ICIG)

作者： Ma, Dongdong Liu, Shaojun Liao, Qingmin Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China

ISBN: (纸本)9783319715988;9783319715971

This paper presents a novel run-based connected components labeling algorithm which uses double-row scan. In this algorithm, the run is defined in double rows and the binary image is scanned twice. In the first scan, provisional labels are assigned to runs according to the connectivity between the current run and runs in the last two rows. Simultaneously, equivalent provisional labels are recorded. Then the adjacent matrix of the provisional labels is generated and decomposed with the Dulmage-Mendelsohn decomposition, to search for the equivalent-label sets in linear time. In the second scan, each equivalent-label set is labeled with a number from 1, which can be efficiently accomplished in parallel. The proposed algorithm is compared with the state-of-the-art algorithms both on synthetic images and real image datasets. Results show that the proposed algorithm outperforms the other algorithms on images with low density of foreground pixels and small amount of connected components.

关键词： Connected Components Labeling (CCL) Image segmentation Sparse matrix decomposition parallel algorithm

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parallelized Flight Path Prediction using a Graphics Processing Unit 12

Parallelized Flight Path Prediction using a Graphics Process...

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12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP)

作者： Gotzinger, Maximilian Pongratz, Martin Rahmani, Amir M. Jantsch, Axel Univ Turku Dept Informat Technol Turku Finland TU Wien Inst Comp Technol Vienna Austria Univ Calif Irvine Dept Comp Sci Irvine CA USA

ISBN: (纸本)9789897582271

Summarized under the term Transport-by-Throwing, robotic arms throwing objects to each other are a visionary system intended to complement the conventional, static conveyor belt. Despite much research and many novel approaches, no fully satisfactory solution to catch a ball with a robotic arm has been developed so far. A new approach based on memorized trajectories is currently being researched. This paper presents an algorithm for real-time image processing and flight prediction. Object detection and flight path prediction can be done fast enough for visual input data with a frame rate of 130 FPS (frames per second). Our experiments show that the average execution time for all necessary calculations on an NVidia GTX 560 TI platform is less than 7.7ms. The maximum times of up to 11.7ms require a small buffer for frame rates over 85 FPS. The results demonstrate that the use of a GPU (Graphics Processing Unit) considerably accelerates the entire procedure and can lead to execution rates of 3.5x to 7.2x faster than on a CPU. Prediction, which was the main focus of this research, is accelerated by a factor of 9.5 by executing the devised parallel algorithm on a GPU. Based on these results, further research could be carried out to examine the prediction system's reliability and limitations (compare (Pongratz, 2016)).

关键词： CUDA GPU Canny RANSAC Image Processing parallel algorithm Flight Path Prediction Based on Memorized Trajectories

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