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Description: This MEX performs 2d bilinear interpolation using an NVIDIA graphics chipset. To compile and run this software, one needs the NVIDIA CUDA Toolkit (http://www.nvidia.com/object/cuda_get.html) and, of course, an NVIDIA graphics card of reasonably modern vintage.
BUILDING INSTRUCTIONS: Change the 'MATLAB' (and if necessary, 'MEX') variables in the Makefile to appropriate values, then simply run 'make' at a prompt and an executable (mex/mexmac/mexmaci/dll?) file will be created.
This code uses your GPU's built-in bilinear texture interpolation capability, and is very fast. For reasonably sized operations (taking, say, a 50x50 matrix up to 1000x1000) CUDA-based code is 5-10x faster than linear interp2 (as tested on a MBP 2.4GHz C2D, GeForce 8600M GT).
With very (VERY) large matrices, however, it has the capability of completely crashing your computer or giving bizarre results. Be careful!
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Size: 37881 |
Author: whitewalter |
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Description: 本工程为sift特征提取的GPU实现，其提取速度比单纯使用CPU计算快20倍，要注意的是显卡要是Nvidia 6以上的-This works for feature extraction sift realize the GPU, and its extraction faster than using the CPU alone is 20 times faster calculation, it is necessary to note that the graphics card if Nvidia 6 or above
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Size: 9003008 |
Author: alenw |
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Description: A very fast FFT for Matlab with GPU
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Size: 5120 |
Author: Includer |
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Description: GPU Code in Matlab-GPU Code in Matlab
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Size: 394240 |
Author: d0238 |
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Description: 基于GPU计算的SVM，VC++源码，包括详细文档说明文件。借用了GPU编程的优势，该代码据作者说比常规的libsvm等算法包的训练速度快13-73倍，预测速度快22-172倍。希望对大家有用-cuSVM is a software package for high-speed (Gaussian-kernelized) Support Vector Machine training and prediction that exploits the massively parallel processing power of Graphics Processors (GPUs). cuSVM is written in NVIDIA s CUDA C-language GPU programming environment, includes implementations of both classification and regression, and performs SVM training (prediction) at 13-73 (22-172) times the rate of state of the art CPU software. Moreover, cuSVM features a Matlab MEX wrapper so that users can access the GPU s power without having to do any "real" programming.
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Size: 879616 |
Author: Sheng |
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Description: The latest generation of high-end video cards off er considerable
computing power using their 100–200 on-card processors, 0.3–1.0+ GB
of RAM, and fast inter-processor communications. One promising
application of this Graphics Processing Unit (GPU) computing
capability is through Matlab and Matlab mex functions. With a properly developed mex function, the user-friendly Matlab interface can be used to perform behind-the-scenes parallel computations on the GPU. -The latest generation of high-end video cards offer considerable computing power using their 100-200 on-card processors, 0.3-1.0+ GB of RAM, and fast inter-processor communications. One promising application of this Graphics Processing Unit (GPU) computing capability is through Matlab and Matlab mex functions. With a properly developed mex function, the user-friendly Matlab interface can be used to perform behind-the-scenes parallel computations on the GPU.
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Size: 166912 |
Author: charlesw0100 |
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Description: 非常有用的GPU计算工具gpumat的教材，可以在matlab调用gpu命令-GPU computing very useful tool gpumat materials, you can call the gpu command in matlab
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Size: 379904 |
Author: 5808598 |
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Description: Fast continuous max-flow algorithm to 2D/3D image segmentation developed in matlab, C and GPU
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Size: 2300928 |
Author: lsx |
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Description: gpu tutorial pct demo tor matlab use and for matlab optimization tool for research student of the state of israel the best in the world
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Size: 3937280 |
Author: shamgar |
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Description: GPU Matlab JACKET编程帮助手册-GPU Matlab JACKET programming help manual
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Size: 315392 |
Author: kakahaha |
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Description: matlab结合GPU的资料，初学者可以看看，很不错的解释,适合参考-matlab GPU combined data, beginners can see, very good explanation for the reference
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Size: 71680 |
Author: 阿泉 |
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Description: MATLAB平台下的CUDA加速库。运用该库可避免在MEX函数中来回拷贝GPU数据，提高MATLAB运行速率。附件给出一个简单的矩阵乘法的示例程序，该程序在GTX TITAN下运行效率比intel i7 3930k提高了10倍。-CUDA MATLAB platform acceleration libraries. Avoid the use of the library can be copied back and forth GPU MEX function data, improve MATLAB run rate. Annex gives an example of a simple matrix multiplication program that runs under the GTX TITAN 10 times more efficient than the intel i7 3930k.
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Size: 379904 |
Author: andy |
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Description: 这个是基于环形拓扑结构的约束微粒群算法，对原来的算法做了改进，只用到了一个邻居节点，效果与基本的微粒群算法大致相当。但是在对多模函数的计算上，优于基本的微粒群算法。这个算法的运行平台是GPUmat,需要的运行环境有：Matlab, GPU, GPUmat, Visual Studio.-This the modified particle swarm optimization algorithm that based on ring toplopy with constrain condition. The lbest of this algorithm only comes itself and its one neighbourhood , the experimental result show its performance as well as simple PSO. However, it is better than simple PSO when it is applied in the multimodal function. The systme environment including: Matlab, GPU, GPUmat, Visual Studio.
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Size: 3072 |
Author: Shiqin Yang |
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Description: 图像处理 并行处理 matlab Since images can be represented by 2D or 3D matrices and the MATLAB processing engine
relies on matrix representation of all entities, MATLAB is particularly suitable for implemen‐
tation and testing of image processing workflows. The Image Processing Toolbox
™
(IPT)
includes all the necessary tools for general-purpose image processing incorporating more than
300 functions which have been optimised to offer good accuracy and high speed of processing.
Moreover, the built-in Parallel Computing Toolbox
™
(PCT) has recently been expanded and
now supports graphics processing unit (GPU) acceleration for some functions of the IPT.
However, for many image processing applications we still need to write our own code, either
in MATLAB or, in the case of GPU-accelerated applications requiring specific control over
GPU resources, in CUDA (Nvidia Corporation, Santa Clara, CA, USA).-the first part is dedicated to some essential tools of the IPT that can be used in
image analysis and assessment as well as in extraction of useful information for further
processing and assessment. These include retrieving information about digital images, image
adjustment and processing as well as feature extraction and video handling. The second part
is dedicated to GPU acceleration of image processing techniques either by using the built-in
PCT functions or through writing our own functions. Each section is accompanied by MAT‐
LAB example code.
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Size: 629760 |
Author: 阿新 |
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Description: 这个是GPU并行学习，自己找到的电子版，纸质版购买好贵的，虽然是英文版的，但是写的比较基础，建议学习并行的可以-This is the GPU parallel learning, find their own electronic version of the paper version of the purchase was expensive, though, is in English, but the written basis of comparison, the proposed parallel learning can look
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Size: 24598528 |
Author: 王 |
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Description: 极限学习机代码（GPU版本），使用MATLAB自带的GPU工具箱-Extreme Learning Machine code (GPU version), the use of MATLAB s own GPU toolbox
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Size: 20480 |
Author: ly |
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Description: 这是在Matlab软件平台下的 GPU程序，进行图像放大的并行运算，使用CUDA来编写程序。(This is in the Matlab software platform under the GPU program, image amplification parallel operation, using CUDA to write programs.)
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Size: 952320 |
Author: 仁仁
|
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Description: 自己写的GPU并行程序和MATLAB 平台混合调用，适合初学者学习MATLAB 和GPU的并行编程(Write their own GPU parallel program and MATLAB platform mixed call, suitable for beginners to learn MATLAB and GPU parallel programming)
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Size: 2561024 |
Author: Bigstrong
|
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Description: fdtdq.m 球面坐标系的fdtd实现程序
fdtdgpu.m 使用gpu并行的fdtd实现程序(The FDTD realization program of fdtdq.m spherical coordinate system
Fdtdgpu.m uses a GPU Parallel FDTD implementation program)
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Size: 13946880 |
Author: tomin |
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Description: 1.在MATLAB中直接实现Marching Cubes;
2.使用了向量化和预分配的概念在MATLAB中优化;
3.用c-mex函数和GPU实现.(1. Marching Cubes is realized directly in MATLAB;
2., the concepts of VQ and pre allocation are optimized in MATLAB.
3. is implemented with c-MEX function and GPU.)
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Size: 97280 |
Author: zyj0-0 |
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