Analysis on Motion Behavior of Spherical Shell in a Periodical Shear Flow Based on CUDA Parallel Computing Technique

2016 ◽  
pp. 1399-1407
Author(s):  
Xuejie Jiang ◽  
Jian Li ◽  
Dongxu Wang ◽  
Jingwu Pan
Keyword(s):  
Parallel Computing ◽  
Shear Flow ◽  
Spherical Shell ◽  
Computing Technique ◽  
Motion Behavior

Dagstuhl-Seminar “Dynamically and Partially Reconfigurable Architectures” (Dynamisch und partiell rekonfigurierbare Architekturen)

2004 ◽  
Vol 46 (4) ◽  
Author(s):  
Jürgen Becker
Keyword(s):  
Information Technology ◽  
Parallel Computing ◽  
Computer Science ◽  
Mobile Communication ◽  
Data Stream ◽  
Electrical Engineering ◽  
Reconfigurable Architectures ◽  
Automotive Application ◽  
Partial Reconfiguration ◽  
Computing Technique

SummaryThe paper addresses people from information technology, electrical engineering, computer science, and related areas. It gives an introduction and classification to fine-, coarse-, as well as multi-grain reconfigurable architectures. This data-stream-based and transport-triggered parallel computing technique in combination with dynamical and partial reconfiguration features demonstrates promising perspectives for future CMOS-based microelectronic solutions in multimedia and infotainment, mobile communication, as well as automotive application domains, among others.

scholarly journals Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
Chao Dong ◽  
Lianfang Tian
Keyword(s):  
Parallel Computing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Hyperspectral Image ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Training Procedure ◽  
Data Set ◽  
Computing Technique

Benefiting from the kernel skill and the sparse property, the relevance vector machine (RVM) could acquire a sparse solution, with an equivalent generalization ability compared with the support vector machine. The sparse property requires much less time in the prediction, making RVM potential in classifying the large-scale hyperspectral image. However, RVM is not widespread influenced by its slow training procedure. To solve the problem, the classification of the hyperspectral image using RVM is accelerated by the parallel computing technique in this paper. The parallelization is revealed from the aspects of the multiclass strategy, the ensemble of multiple weak classifiers, and the matrix operations. The parallel RVMs are implemented using the C language plus the parallel functions of the linear algebra packages and the message passing interface library. The proposed methods are evaluated by the AVIRIS Indian Pines data set on the Beowulf cluster and the multicore platforms. It shows that the parallel RVMs accelerate the training procedure obviously.

A Hybrid Parallelizable Algorithm for Computer Simulation of Rigid Body Molecular Dynamics

2007 ◽  
Author(s):  
Shanzhong Duan
Keyword(s):  
Molecular Dynamics ◽  
Computer Simulation ◽  
Parallel Computing ◽  
Rigid Body ◽  
Computing System ◽  
Rigid Body Dynamics ◽  
Constraint Forces ◽  
Explicit Determination ◽  
Motion Behavior

Molecular dynamics is effective for a nano-scale phenomenon analysis. This paper presents a hybrid parallelizable algorithm for the computer simulation of the motion behavior of molecular chain and open-tree structure on parallel computing system. The algorithm is developed from an approach of rigid body dynamics, in which interbody constraints are exposed so that a system of largely independent multibody subchains is formed. The increased parallelism is obtainable through bringing interbody constraints to evidence and the explicit determination of the associated constraint forces combined with a sequential O(n) procedure. Each subchain then is assigned to a processor for parallel computing. The algorithm offers a sequential O(n) performance if there is only one processor available. The algorithm has O(log2n) computational efficiency if there are as many processors available as number for molecular bodies. For most common scenario, the algorithm will give a computational complexity between O(n) and O(log2n) if number of available processor is less than number of molecular bodies.

Highly efficient parallel computing technique for electromagnetic PIC simulation in Linux system

2012 ◽  
Vol 24 (9) ◽  
pp. 2225-2229
Author(s):  
彭凯 Peng kai ◽  
夏蒙重 Xia Mengzhong ◽  
刘大刚 Liu Dagang ◽  
周俊 Zhou Jun
Keyword(s):  
Parallel Computing ◽  
Computing Technique ◽  
Pic Simulation ◽  
Highly Efficient

Parallel Route Optimization Algorithm of Central Guidance

2013 ◽  
Vol 380-384 ◽  
pp. 1571-1575
Author(s):  
Hong Chen ◽  
Hu Xing Zhou ◽  
Juan Meng
Keyword(s):  
Parallel Computing ◽  
Real Time ◽  
Data Storage ◽  
Route Optimization ◽  
Guidance System ◽  
Network Data ◽  
Traffic Network ◽  
Computing Technique ◽  
The Real ◽  
Guangzhou City

To solve the problem that the central guidance system takes too long time to calculate the shortest routes between all node pairs of network which can not meet the real-time demand of central guidance, this paper presents a central guidance parallel route optimization method based on parallel computing technique involving both route optimization time and travelers preferences by means of researching three parts: network data storage based on an array, multi-level network decomposition with travelers preferences considered and parallel shortest route computing of deque based on messages transfer. And based on the actual traffic network data of Guangzhou city, the suggested method is verified on three parallel computing platforms including ordinary PC cluster, Lenovo server cluster and HP workstations cluster. The results show that above three clusters finish the optimization of 21.4 million routes between 5631 nodes of Guangzhou city traffic network in 215, 189 and 177 seconds with the presented method respectively, which can completely meet the real-time demand of the central guidance.

A multilevel index optimization method for fast kinematic calibration configuration of serial manipulators based on Compute Unified Device Architecture parallel computing

2020 ◽  
Vol 234 (23) ◽  
pp. 4708-4724
Author(s):  
Xuejie Jiang ◽  
Lijin Fang ◽  
Yue Gao
Keyword(s):  
Parallel Computing ◽  
Optimization Method ◽  
Evaluation Criterion ◽  
Kinematic Calibration ◽  
Compute Unified Device Architecture ◽  
Multilevel Optimization ◽  
Computing Technique ◽  
Serial Manipulators ◽  
Device Architecture ◽  
Calibration Accuracy

The kinematic calibration accuracy of serial manipulators is affected by the error expression ability of the selected measurement configurations and non-geometric errors such as joint disturbance, measurement noise, etc. Based on the observability of configurations, deviation of identifiable parameters, and calibration robustness, this paper proposes a multilevel evaluation criterion for measurement configuration optimization. In addition, based on the Compute Unified Device Architecture (CUDA) parallel computing technique, the most time-consuming Jacobian matrix calculation program in the algorithm is modified, and an efficient optimization algorithm for measurement configurations is established, to guarantee the feasibility of the evaluation criterion. Combined with CUDA algorithm, fast calibration is implemented with fewer measurement points and relatively higher accuracy, by means of multilevel optimization. The results illustrate the effectiveness and the universality of the proposed multilevel evaluation criterion. The criterion can be applied in calibration experiments of multi-degree of freedom (DOF) serial manipulators with complex structures.

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