共查询到20条相似文献,搜索用时 31 毫秒
1.
Visual and interactive data exploration requires fast and reliable tools for embedding of an original data space in 3(2)‐dimensional Euclidean space. Multidimensional scaling (MDS) is a good candidate. However, owing to at least O( M2) memory and time complexity, MDS is computationally demanding for interactive visualization of data sets consisting of order of 10 4 objects on computer systems, ranging from PC with multicore CPU processor, graphics processing unit (GPU) board to midrange MPI clusters. To explore interactively data sets of that size, we have developed novel efficient parallel algorithms for MDS mapping based on virtual particle dynamics. We demonstrate that the performance of our MDS algorithms implemented in compute unified device architecture environment on a PC equipped with a modern GPU board (Tesla M2090, GeForce GTX 480) is considerably faster than its MPI/OpenMP parallel implementation on the modern midrange professional cluster (10 nodes, each equipped with 2x Intel Xeon X5670 CPUs). We also show that the hybridized two‐level MPI/CUDA implementation, run on a cluster of GPU nodes, can additionally provide a linear speedup. Copyright 2013 John Wiley & Sons, Ltd. 相似文献
2.
The video coding standard H.264 supports video compression with a higher coding efficiency than previous standards. However, this comes at the expense of an increased encoding complexity, in particular for motion estimation which becomes a very time consuming task even for today's central processing units (CPU). On the other hand, modern graphics hardware includes a powerful graphics processing unit (GPU) whose computing power remains idle most of the time. In this paper, we present a GPU based approach to motion estimation for the purpose of H.264 video encoding. A small diamond search is adapted to the programming model of modern GPUs to exploit their available parallel computing power and memory bandwidth. Experimental results demonstrate a significant reduction of computation time and a competitive encoding quality compared to a CPU UMHexagonS implementation while enabling the CPU to process other encoding tasks in parallel. 相似文献
3.
The emergence of parallel array processing, both in software methodology and hardware technology, opens new avenues for the implementation and optimization of systems for interactive computer graphics. The Q-spline interpolation method is presented, designed for incremental curve definition, local curve modification, “on-the-curve” control points and computational efficiency in array processing environment. The implementation and performance of the algorithms in the environment of a general purpose interactive computer graphics system is described. 相似文献
4.
特征提取与描述是众多计算机视觉应用的基础。局部特征提取与描述因像素级处理产生的高维计算而导致其计算复杂、实时性差,影响了算法在实际系统中的应用。研究了局部特征提取与描述中的关键共性计算模块——图像金字塔机制及图像梯度计算。基于NVIDIA GPU/CUDA架构设计并实现了共性模块的并行计算,并通过优化全局存储、纹理存储及共享存储的访问方式进一步实现了其高效计算。实验结果表明,基于GPU的图像金字塔和图像梯度计算比CPU获得了30倍左右的加速,将实现的图像金字塔和图像梯度计算应用于HOG特征提取与描述算法,相比CPU获得了40倍左右的加速。该研究对于基于GPU实现局部特征的高速提取与描述具有现实意义。 相似文献
5.
Genetic Programming (GP) is a computationally intensive technique which is also highly parallel in nature. In recent years, significant performance improvements have been achieved over a standard GP CPU-based approach by harnessing the parallel computational power of many-core graphics cards which have hundreds of processing cores. This enables both fitness cases and candidate solutions to be evaluated in parallel. However, this paper will demonstrate that by fully exploiting a multi-core CPU, similar performance gains can also be achieved. This paper will present a new GP model which demonstrates greater efficiency whilst also exploiting the cache memory. Furthermore, the model presented in this paper will utilise Streaming SIMD Extensions to gain further performance improvements. A parallel version of the GP model is also presented which optimises multiple thread execution and cache memory. The results presented will demonstrate that a multi-core CPU implementation of GP can yield performance levels that match and exceed those of the latest graphics card implementations of GP. Indeed, a performance gain of up to 420-fold over standard GP is demonstrated and a threefold gain over a graphics card implementation. 相似文献
6.
H.264/AVC video encoders have been widely used for its high coding efficiency. Since the computational demand proportional to the frame resolution is constantly increasing, it has been of great interest to accelerate H.264/AVC by parallel processing. Recently, graphics processing units (GPUs) have emerged as a viable target for accelerating general purpose applications by exploiting fine-grain data parallelisms. Despite extensive research efforts to use GPUs to accelerate the H.264/AVC algorithm, it has not been successful to achieve any speed-up over the x264 algorithm that is known as the fastest CPU implementation, mainly due to significant communication overhead between the host CPU and the GPU and intra-frame dependency in the algorithm. In this paper, we propose a novel motion-estimation (ME) algorithm tailored for NVIDIA GPU implementation. It is accompanied by a novel pipelining technique, called sub-frame ME processing, to effectively hide the communication overhead between the host CPU and the GPU. Further, we incorporate frame-level parallelization technique to improve the overall throughput. Experimental results show that our proposed H.264 encoder has higher performance than x264 encoder. 相似文献
7.
由于GPU(图形处理器)性能的大幅提高和可编程性的发展,基于GPU的光线追踪算法逐渐成为研究热点。光线追踪算法需要的计算量大,基于此,分析了光线追踪算法的基本原理,在NVIDIA公司的CUDA(计算统一设备体系结构)环境下采用均匀栅格法作为加速结构实现了光线追踪算法。实验结果表明,该计算模式相对于传统基于CPU的光线追踪算法具有更快的整体运算速度,GPU适合处理高密度数据计算。 相似文献
8.
计算机图形生成的并行处理是结合图形学、并行处理及并行算法交叉而产生的一个新课题.本文首先介绍了近几年来这一领域的发展,然后针对计算机图形学领域发展最为迅速的分支之一——物理场的图形显示,利用最新提出的并行算法进行了研究和实现,并就其在两种并行处理机环境下的具体实现进行了结果分析. 相似文献
9.
空间插值是地理信息系统(GIS)空间分析中计算复杂且耗时的操作,因此无法满足实时性的要求。随着图形处理器(GPU)浮点计算能力的大幅提高,GPU通用计算已成为处理GIS领域内复杂计算的研究热点。为实时化一些传统低效的算法提供了良好的契机。利用GPU在并行计算上的优势,将反距离加权法插值算法映射到了统一计算设备架构(CUDA)并行编程架构。首先在GPU中建立二级索引使计算层次得到了合理的划分,然后利用多线程分块策略执行并行插值计算。最后通过实验表明,该方法的插值误差与CPU方法相比能控制在10-6数量级,并且在插值半径较大插值数据较多的情况下,该算法可达到40倍以上的加速比。充分证明了该方法的正确性及高效性。 相似文献
10.
Gadget is a simulation application for N‐body and smoothed particle hydrodynamics problems in cosmology, and it is widely applied in solving series of cosmological problems. N‐body focuses on the motion of the interaction of N particles, and smoothed particle hydrodynamics is a fluid simulation algorithm that studies the movement of fluid through particle simulation. Most scholars focus their attention on accelerating Gadget on multi‐core CPU or graphics processing units (GPUs) platforms. However, these research activities failed to achieve CPU–GPU hybrid computing, which resulted in tremendous waste of CPU computing resources. In this paper, we propose a CPU–GPU hybrid parallel strategy to accelerate Gadget‐2, a massively parallel structure formation code for cosmological simulations. This strategy uses CPU and GPU to process the calculation of short‐range force. To ensure CPU and GPU workload balance, a dynamic task allocation scheme is proposed according to the computational performance difference between the CPU and GPU. Experimental results showed that our CPU–GPU hybrid parallel strategy achieved an overall speedup factor of 18.6 and a partial speedup factor for short‐range force calculation of 28.35 compared with a single‐core CPU implementation for particles in million‐size magnitudes. Moreover, compared with a GPU platform that contained 12 CPU cores and one GPU, our hybrid parallel strategy obtained overall speedup and partial speedup factors of 6% and 20%, respectively. Furthermore, the scalability of the hybrid strategy is very fine – its performance will be enhanced when the problem scale is increasing. However, this strategy also has its limitation that the performance enhancement will be decreasing if the ratio(the number of CPU cores divides that of the GPU cards) reduces. Finally, in our hybrid strategy, the CPU coefficient of utilization improved by 17.14% or better. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
11.
In this paper, we present the graphics processing unit (GPU)‐based parallel implementation of visibility calculation from multiple viewpoints on raster terrain grids. Two levels of parallelism are introduced in the GPU kernels — parallel traversal of visibility rays from a single viewpoint and parallel processing of viewpoints. The obtained visibility maps are combined in parallel using the selected logical operator. A comparison with multi‐threaded CPU implementation is performed to establish the expected speed‐ups of viewshed construction when the source and destination types are sets of scattered locations, paths, or regions. The results demonstrate that using the GPU, the acceleration of an order of magnitude can be achieved on average with both point sampling and bilinear filtering of the elevation map. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
12.
Topology optimization has proven to be viable for use in the preliminary phases of real world design problems. Ultimately, the restricting factor is the computational expense since a multitude of designs need to be considered. This is especially imperative in such fields as aerospace, automotive and biomedical, where the problems involve multiple physical models, typically fluids and structures, requiring excessive computational calculations. One possible solution to this is to implement codes on massively parallel computer architectures, such as graphics processing units (GPUs). The present work investigates the feasibility of a GPU-implemented lattice Boltzmann method for multi-physics topology optimization for the first time. Noticeable differences between the GPU implementation and a central processing unit (CPU) version of the code are observed and the challenges associated with finding feasible solutions in a computational efficient manner are discussed and solved here, for the first time on a multi-physics topology optimization problem. The main goal of this paper is to speed up the topology optimization process for multi-physics problems without restricting the design domain, or sacrificing considerable performance in the objectives. Examples are compared with both standard CPU and various levels of numerical precision GPU codes to better illustrate the advantages and disadvantages of this implementation. A structural and fluid objective topology optimization problem is solved to vary the dependence of the algorithm on the GPU, extending on the previous literature that has only considered structural objectives of non-design dependent load problems. The results of this work indicate some discrepancies between GPU and CPU implementations that have not been seen before in the literature and are imperative to the speed-up of multi-physics topology optimization algorithms using GPUs. 相似文献
13.
This paper presents a low-latency algorithm designed for parallel computer architectures to compute the scalar multiplication of elliptic curve points based on approaches from cryptographic side-channel analysis. A graphics processing unit implementation using a standardized elliptic curve over a 224-bit prime field, complying with the new 112-bit security level, computes the scalar multiplication in 1.9?ms on the NVIDIA GTX 500 architecture family. The presented methods and implementation considerations can be applied to any parallel 32-bit architecture. 相似文献
15.
Algorithmic skeletons simplify software development: they abstract typical patterns of parallelism and provide their efficient implementations, allowing the application developer to focus on the structure of algorithms, rather than on implementation details. This becomes especially important for modern parallel systems with multiple graphics processing units (GPUs) whose programming is complex and error-prone, because state-of-the-art programming approaches like CUDA and OpenCL lack high-level abstractions. We define a new algorithmic skeleton for allpairs computations which occur in real-world applications, ranging from bioinformatics to physics. We develop the skeleton’s generic parallel implementation for multi-GPU Systems in OpenCL. To enable the automatic use of the fast GPU memory, we identify and implement an optimized version of the allpairs skeleton with a customizing function that follows a certain memory access pattern. We use matrix multiplication as an application study for the allpairs skeleton and its two implementations and demonstrate that the skeleton greatly simplifies programming, saving up to 90 % of lines of code as compared to OpenCL. The performance of our optimized implementation is up to 6.8 times higher as compared with the generic implementation and is competitive to the performance of a manually written optimized OpenCL code. 相似文献
16.
This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on
the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration
over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware,
the CPU is freed up to run other computations in parallel. Our GPU-based KLT implementation tracks about a thousand features
in real-time at 30 Hz on 1,024 × 768 resolution video which is a 20 times improvement over the CPU. The GPU-based SIFT implementation
extracts about 800 features from 640 × 480 video at 10 Hz which is approximately 10 times faster than an optimized CPU implementation. 相似文献
17.
This paper presented two schemes of parallel 2D discrete wavelet transform (DWT) on Compute Unified Device Architecture graphics processing units. For the first scheme, the image and filter are transformed to spectral domain by using Fast Fourier Transformation (FFT), multiplied and then transformed back to space domain by using inverse FFT. For the second scheme, the image pixels are convolved directly with filters. Because there is no data relevance, the convolution for data points on different positions could be executed concurrently. To reduce data transfer, the boundary extension and down‐sampling are processed during data loading stage, and transposing is completed implicitly during data storage. A similar skill is adopted when parallelizing inverse 2D DWT. To further speed up the data access, the filter coefficients are stored in the constant memory. We have parallelized the 2D DWT for dozens of wavelet types and achieved a speedup factor of over 380 times compared with that of its CPU version. We applied the parallel 2D DWT in a ring artifact removal procedure; the executing speed was accelerated near 200 times compared with its CPU version. The experimental results showed that the proposed parallel 2D DWT on graphics processing units can significantly improve the performance for a wide variety of wavelet types and is promising for various applications. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
18.
We introduce a new approach for defining continuous non‐oriented gradient fields from discrete inputs, a fundamental stage for a variety of computer graphics applications such as surface or curve reconstruction, and image stylization. Our approach builds on a moving least square formalism that computes higher‐order local approximations of non‐oriented input gradients. In particular, we show that our novel isotropic linear approximation outperforms its lower‐order alternative: surface or image structures are much better preserved, and instabilities are significantly reduced. Thanks to its ease of implementation (on both CPU and GPU) and small performance overhead, we believe our approach will find a widespread use in graphics applications, as demonstrated by the variety of our results. 相似文献
19.
The capability for understanding data passes through the ability of producing an effective and fast classification of the information in a time frame that allows to keep and preserve the value of the information itself and its potential. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. A powerful tool is provided by self-organizing maps (SOM). The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns. Because of its time complexity, often using this method is a critical challenge. In this paper we propose a parallel implementation for the SOM algorithm, using parallel processor architecture, as modern graphics processing units by CUDA. Experimental results show improvements in terms of execution time, with a promising speed up, compared to the CPU version and the widely used package SOM_PAK. 相似文献
20.
In this work, we developed a parallel algorithm to speed up the resolution of differential matrix Riccati equations using a backward differentiation formula algorithm based on a fixed‐point method. The role and use of differential matrix Riccati equations is especially important in several applications such as optimal control, filtering, and estimation. In some cases, the problem could be large, and it is interesting to speed it up as much as possible. Recently, modern graphic processing units (GPUs) have been used as a way to improve performance. In this paper, we used an approach based on general‐purpose computing on graphics processing units. We used NVIDIA © GPUs with unified architecture. To do this, a special version of basic linear algebra subprograms for GPUs, called CUBLAS, and a package (three different packages were studied) to solve linear systems using GPUs have been used. Moreover, we developed a MATLAB © toolkit to use our implementation from MATLAB in such a way that if the user has a graphic card, the performance of the implementation is improved. If the user does not have such a card, the algorithm can also be run using the machine CPU. Experimental results on a NVIDIA Quadro FX 5800 are shown. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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