Read GPU-based Parallel Implementation of Swarm Intelligence Algorithms - Ying Tan file in ePub
Related searches:
C++ accelerated massive parallelism (c++ amp) is a library that accelerates execution of c++ code by exploiting the data-parallel hardware on gpus.
Citeseerx - document details (isaac councill, lee giles, pradeep teregowda): abstract—we consider flexible decoder implementation of low density parity check (ldpc) codes via compute-unified-device-architecture (cuda) programming on graphics processing unit (gpu), a research subject of considerable recent interest.
The slow-down in moore's law, and the availability of graphics processing units (gpus) capable of conducting general-purpose computations at high speed, has sparked considerable research efforts into the development of gpu-based aco implementations.
Hardware-optimized parallel implementation of the radix sort algorithm that results in a significant mance, recently, parallel sorting, especially gpu based sort-.
Even within such approaches, however, dci computation has to be performed for a huge number of times; thus, an efficient implementation becomes a mandatory requirement. Considering that a candidate relevant set’s dci can be computed independently of the others, we propose a gpu-based massively parallel implementation of dci computation.
By using new developed gpu-based min-reduction data parallel primitive in the key step of the algorithm, higher efficiency is achieved. Experimental results show that we obtain about 2 times speedup on nvidia gtx260 gpu over the cpu implementation and 3 times speedup over non-primitives gpu implementation.
Learn about using gpu-enabled matlab functions, executing nvidia cuda code from matlab and performance considerations.
Gpu-based parallel multi-objective particle swarm optimization. International journal of artificial intelligence, 7(a11):125--141.
Gpu-based parallel implementation of a growing self-organizing.
A fast and generic gpu-based parallel reduction implementation.
The main com-ponents of our gpu-based parallelization of the adi scheme are (i) an e cient parallel implementation of the explicit euler predictor step, and (ii) a parallel solver for the independent tridiagonal systems arising in the three implicit, but unidirectional, corrector steps.
Gpu-based parallel implementation of swarm intelligence algorithms [tan, ying] on amazon. Gpu-based parallel implementation of swarm intelligence algorithms.
This paper proposes a parallel gpu-based version of vca that alleviates considerably the computational burden, allowing a significant increase in processing speed.
Gaura “parallel implementation of recursive background modeling technique in cuda for tracking moving objects in video traffic surveillance”, annual doctoral workshop on mathematical and engineering methods in computer science, brno university of technology, czechia, november 2008.
A gpu-based implementation, obviously, does not change the general properties of the algorithms. As well, we give for granted that gpu-based implementation of both algorithm and fitness function produces a significant speed-up with respect to a sequential implementation.
Sep 21, 2017 by taking advantage of multi-level parallelism and the compute unified device architecture (cuda) parallel kernel-call library, gpu-aracne.
We implemented a fluid simulation on a cpu with openmp and on a gpu with cuda from scratch, and compared the performance of the two implementations.
In this project, we present a cuda-based parallel algorithm, where scan's computation steps are carefully redesigned.
Gpu-based parallel computation of integral properties of volumetrically digitized objects view/open author metadata abstract uri collections.
Request pdf gpu-based parallel implementation of sar imaging synthetic aperture radar (sar) is an all-weather remote sensing technology and occupies a great position in disaster observation.
This paper presents a comprehensive review of gpu-based parallel sias in accordance with a newly proposed taxonomy. Critical concerns for the efficient parallel implementation of sias are also described in detail. Moreover, novel criteria are also proposed to evaluate and compare the parallel implementation and algorithm performance universally.
Jun 24, 2016 “a cuda-based parallel implementation of k-nearest neighbor algorithm”. In cyber-enabled distributed computing and knowledge discovery,.
Buy gpu-based parallel implementation of swarm intelligence algorithms online at an affordable price.
Read on for an introductory overview to gpu-based parallelism, the cuda framework, and some thoughts on practical implementation.
Two gpu‐based implementations of the quicksort were presented in literature: the gpu‐quicksort, a compute‐unified device architecture (cuda) iterative implementation, and the cuda dynamic parallel (cdp) quicksort, a recursive implementation provided by nvidia corporation.
Gpu-based parallel implementation of the max-min ant system - rskinderowicz /gpu-based-mmas.
Dec 3, 2009 for large data sets, the common approach to pca computation is based on the standard nipals-pca algorithm, which unfortunately suffers from.
The canny method, sobel and prewitt filters, and the roberts' cross technique are some examples of edge detection algorithms that are widely used in image.
Its gpu-based implementation is presented in the “methods” section, and this implementation will be referred as “gpu-cassert” throughout the paper. Before we start to explain gpu-cassert, we will provide a short overview of how to perform computations using gpu devices and the cuda architecture.
Purchase gpu-based parallel implementation of swarm intelligence algorithms - 1st edition.
We propose a novel unifying scheme for parallel implementation of articulated robot dynamics algorithms. It is based on a unified lie group notation for deriving the equations of motion of articulated robots, where various well-known forward algorithms differ only by their joint inertia matrix inversion strategies. This new scheme leads to a unified abstraction of state-of-the-art forward.
The weather research and forecasting (wrf) is a system of numerical weather prediction and atmospheric simulation with dual purposes for forecasting and research. The wrf software infrastructure consists of several components such as dynamic solvers and physical simulation modules.
Gpu-based implementations of several typical swarm intelligence algorithms such as pso, fwa, ga, de, and aco are presented and having described the implementation details including parallel models, implementation considerations as well as performance metrics are discussed.
According to the gpu-based acceleration method of the invention, fine-granularity parallel implementation of existing main image feature extraction algorithms is performed on gpus, and optimized acceleration can be performed according to the features of the gpus; collaborative work mechanisms of asynchronous assembly lines are adopted to make.
Nowadays reservoir simulators are indispensable tools to reservoir engineers. They are widely used in the optimization and prediction of oil and gas production.
An r-tree is a data structure for organizing and querying multi-dimensional non-uniform and overlapping data. Efficient parallelization of r-tree is an important problem due to societal applications such as geographic information systems (gis), spatial database management systems, and vlsi layout which employ r-trees for spatial analysis tasks such as map-overlay.
Gpu-based implementations of several typical swarm intelligence algorithms such as pso, fwa, ga, de, and aco are presented and having described the implementation details including parallel models.
This approach uses skewing transformation for traversal and calculation of the dynamic programming matrix. We compare the execution time of sequential cpu based implementation with two parallel gpu based implementations: single-kernel invocation with lock-free block synchronization and multi-kernel invocation at block-synchronization points.
Gpu-based parallel implementation of swarm intelligence algorithms combines and covers two emerging areas attracting increased attention and applications: graphics processing units (gpus) for general-purpose computing (gpgpu) and swarm intelligence.
Read gpu-based parallel implementation of swarm intelligence algorithms by ying tan with a free trial.
Towards gpu-based parallelized hybridization design implementation of filtering algorithms. The concurrent way of solving any problem is preferable to everyone nowadays. The objective of gpu-based parallelized hybridization design is to produce recommendations in parallel and combine the results.
Gpu-based parallel implementation of vlbi correlator for deep space exploration system.
Finally, cuda is a framework for parallel computing on nvidia gpus, based on opencl for nvidia gpus is implemented on the top of cuda, so, yes,.
Gpu based parallel implementation for ultrahigh speed execution. Simut is capable of utilizing the graphical processing unit (gpu) for simulation. Graphics card with opencl support is needed to use this feature.
Gpu-based parallel implementation of k-means clustering algorithm for image segmentation. Abstract: clustering algorithms group a dataset into clusters that.
Post Your Comments: