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Description: CUDA入门必备：包括CUDA安装方法，语法高亮，工程Wizard文件，参考手册和编程文档。-Entry must CUDA: CUDA installation methods including, syntax highlighting, project Wizard documents, reference manuals and program documentation.
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Size: 2974720 |
Author: 鲍协浩 |
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Description: CUDA(GPU计算)开发资料，包括3份文档和一份快速开发设置工具，文档包括有CUDA环境设置，CUDA简易编程以及非常有名的深入浅出谈CUDA技术一文。-CUDA (GPU computing) development information, including three copies of the document and a set for rapid development tools, documentation including CUDA environment settings, CUDA programming easy and very well-known visitors to learn about CUDA technology, a text.
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Size: 1620992 |
Author: llc |
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Description: The goal of this lab is to install CUDA on a machine that does not contain a graphics
card support Cuda, and also to install the emulator Cuda in visual studio and set it up.
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Size: 2772992 |
Author: imad |
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Description: deviceQuery确认CUDA编译环境是否确定搭建完成，能够进行CUDA工程编程-deviceQuery confirm CUDA compiler environment is set up to determine the completion of works to the CUDA programming
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Size: 11323392 |
Author: 王杉杉 |
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Description: 一个基础的CUDA例子，绘画出julia集合。-One based on CUDA example, draw julia set.
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Size: 2048 |
Author: weijian |
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Description: 稀疏矩阵的DIA/ELLPACK/COO/CSR/HYB表示形式，以及各表示形式下的稀疏矩阵乘法（稀疏大矩阵*矢量）的CUDA实现。对于矩阵中每一行稀疏元素个数较统一的情况，ELLPACK表示最佳，其次是HYB（ELL+COO）。
CUDA™ 是一种由NVIDIA推出的通用并行计算架构，该架构使GPU能够解决复杂的计算问题。 它包含了CUDA指令集架构（ISA）以及GPU内部的并行计算引擎。 开发人员现在可以使用C语言来为CUDA™ 架构编写程序-Sparse matrix DIA/ELLPACK/COO/CSR/HYB representation, as well as the representation of the sparse matrix multiplication (large sparse matrix* vector)' s CUDA implementation. For each row of the sparse matrix representing the number of elements the case of unification, ELLPACK that the best, followed by HYB (ELL+COO). NVIDIA CUDA ™ is introduced by a general purpose parallel computing architecture that makes the GPU to solve complex computational problems. It contains the CUDA Instruction Set Architecture (ISA) and the GPU parallel computing engine. Developers can now use the C language to write programs for the CUDA ™ architecture
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Size: 3282944 |
Author: lipeng |
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Description: opencl编写的blas，和Cuda的cublas类似。-This repository houses the code for the OpenCL™ BLAS portion of clMath. The complete set of BLAS level 1, 2 & 3 routines is implemented.
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Size: 1441792 |
Author: 杨 |
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Description: 基于GOU的Julia集，用CUDA C编程，要与GPU高性能编程CUDA实战这本书一起学CUDA-Based GOU Julia set with CUDA C programming, and to learn together CUDA CUDA GPU programming high-performance combat this book
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Size: 3441664 |
Author: 耳双 |
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Description: Robust Non-negative Dictionary Learning for Visual Tracking
The provided codes could be either embedded into the benchmark framework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence.
To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m.
To run on individual video, you need to modify the dataPath and title in run_individual.m.
If you run MATLAB version after 2012, and have a CUDA compatible GPU installed, you may enjoy the fast computation speed by GPU, just set useGPU to true in trackparam_DLT.m and run_individual.m!
-Robust Non-negative Dictionary Learning for Visual Tracking
The provided codes could be either embedded into the benchmark framework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence.
To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m.
To run on individual video, you need to modify the dataPath and title in run_individual.m.
If you run MATLAB version after 2012, and have a CUDA compatible GPU installed, you may enjoy the fast computation speed by GPU, just set useGPU to true in trackparam_DLT.m and run_individual.m!
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Size: 22211584 |
Author: mohit |
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Description: 此程序使用CUDA并行语言完成矩阵的加法。矩阵维数为3×3矩阵。矩阵维数可以更改。但是同时矩阵的处置也要手动设置。-This program uses the CUDA parallel language to complete the addition matrix. 33 matrix matrix dimensions. You can change the dimension of the matrix. However, while the disposal of the matrix must be set manually.
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Size: 1024 |
Author: |
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Description: cuda入门范例（julia集的计算）,CUDA加速计算，简单的范例-cuda Start sample (julia set computing), CUDA accelerated computing, simple example
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Size: 7109632 |
Author: 刘忠源 |
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Description: 基于cuda的level set 代码，里面包含详细研究报告-CUDA based level set code, including research report.
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Size: 3828736 |
Author: jsphone |
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Description: CUDA并行计算JULIA集，是GPU高性能计算的代码-CUDA Parallel Computing JULIA set, the code GPU High Performance Computing
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Size: 1024 |
Author: chengpx |
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Description: 启动内核--从“myFirstKernel”模板开始。
Part1：使用指针d_a为内核的结果分配设备内存。
Part2：使用1-D的1-D网格来配置和启动内核
线程块。
Part3：让每个线程设置一个d_a的元素，如下所示：
idx = blockIdx.x * blockDim.x + threadIdx.x d_a [idx] = 1000 * blockIdx.x + threadIdx.x
Part4：将d_a中的结果复制回主机指针h_a。
Part5：验证结果是否正确。(Start the kernel -- start with the myFirstKernel template.
Part1: use pointer d_a to allocate device memory for the results of the kernel.
Part2: use the 1-D 1-D grid to configure and start the kernel
Thread block.
Part3: let each thread set an element of d_a, as shown below:
IDX = blockIdx.x * blockDim.x + threadIdx.x d_a = [idx] = 1000 * blockIdx.x + threadIdx.x
Part4: copy the results from d_a back to the host pointer h_a.
Part5: is the validation correct?.)
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Size: 6144 |
Author: p-yang
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Description: CUDA学习的电子书籍,很适合初级的学习，当初学校搭建光子模拟的时候，我接触到的书籍(CUDA learning electronic books, it is suitable for primary learning, when the school set up photonic simulation, I contacted the books)
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Size: 46187520 |
Author: 我亦追红
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