cuda tools

CUDA 3, CUDAPreface The thread organization form is crucial to the program performance. This blog post mainly introduces the thread organization form in the following situations: 2D grid 2D block Thread Index Generally, a matrix is linearly stored in global memory and linear with rows: In kernel, the unique index of a thread is very useful. To determine the index of a thread, we take 2D as an example: Thread and block Indexes Element coordinates

=2; - float*x_h, *x_d, *y_h, *Y_d; -X_h = (float*) malloc (n *sizeof(float)); -Y_h = (float*) malloc (n *sizeof(float)); + for(inti =0; I ) - { +X_h[i] = (float) I; AY_h[i] =1.0; at } -Cudamalloc (x_d, n *sizeof(float)); -Cudamalloc (y_d, n *sizeof(float)); -cudamemcpy (X_d, X_h, n *sizeof(float), cudamemcpyhosttodevice); -cudamemcpy (Y_d, Y_h, n *sizeof(float), cudamemcpyhosttodevice); -Saxpy 1, ->>>(A, x_d, Y_d, n); incudamemcpy (Y_h, Y_d, n *sizeof(float), cudamemcpydeviceto

OpenCV read the picture and pass the picture data to Cuda processing #include Reference code: Calculate PI #include

computing to extend parallel computing from large clusters to ordinary graphics cards. Allows users to run larger parallel programs with a notebook with GeForce graphics card. The advantage of using a video card is that power consumption is very low and inexpensive compared to large clusters, but performance is outstanding. Take my Notebook For example, Geforce 610M, with the Devicequery program test, you can get the following hardware parameters: Computing power up to 48x0.95 = 45.6 GFLOPS.

Ubuntu Gnome 15.04/ubuntu 12.04 Cuda 7.0 Experience StickerBecause recently to run Caffe, there are to run some Cuda program, on the side to learn the configuration, all the way to install it is not easy, dual system (Window 7+ubuntu), make a note to stay with: Premise work: Already installed dual system, if not installed well, refer to the following: Hard drive installation and USB drive inst

explanation of the relationship between Cuda and opencl. They are not conflicting, but inclusive. Opencl is an API. At the first level, the Cuda architecture is at a higher level. In this architecture, whether it is an API like opencl or dx11, or computing like C, Fortran, or dx11, are supported. As program developers, they generally only understand these languages or APIs and can develop their own program

. ~$ nvcc -Vnvcc: NVIDIA (R) Cuda compiler driverCopyright (c) 2005-2014 NVIDIA CorporationBuilt on Thu_Jul_17_21:41:27_CDT_2014Cuda compilation tools, release 6.5, V6.5.12 5.3. device identification Use the devicequery compiled by Cuda sample for verification. Devicequery is in the ~/install/NVIDIA_CUDA-6.5_Samples/bin/x86_64/linux/release$ ./deviceQuery./dev

= $ LD_LIBRARY_PATH:/usr/lib/nvidia-cuda-toolkit/lib:/libExport PATHExport LD_LIBRARY_PATHReboot the computer. Install pyrit-cudaThe wo will compile a pyrit with cuda, download the source from here. Https://code.google.com/p/pyrit/downloads/list We nedd both pyrit-0.4.0.tar.gz and cpyrit-cuda-0.4.0.tar.gz, and wo need soom lib and

This image processing tool which is used to do color transform by a 3D LUT was requested by a friend several days ago, I 'd like to release it now. I have implemented the tetrahedral interpolation method as [1], but my performance of my program is

each Cuda C extension and How to Write Cuda software that delivers truly outstanding performance. Major topics covered include Parallel Programming Thread cooperation Constant memory and events Texture memory Graphics interoperability Atomics Streams Cuda C on multiple GPUs Advanced atomics Additional

Install Nvidia Driver and CUDA Toolkit on CentOS 6 Posted on May 6, 2012 (Update: have posted a MUCH simpler method of driver install. Steps for CUDA toolkit install have to be followed as given in this post, I. e., bulleted step #10-19) Although the topic has been addressed succinctly in a CentOS forum post, there are certain things like plymouth uth configuration post Nvidia driver install, etc. which I f

install the time is this, so this step is not required, no words installed separately.Then do some verification work:1. View the driver version of the video card$ cat/proc/driver/nvidia/version2. View the version of the NVCC compiler$ nvcc-v I3. Compile CUDA Sample code: $ cd ~/nvidia_cuda-6.5_samples, $ sudo make4. After all compilation is complete, enter samples/bin/x86_64/linux/release and sudo to run devicequery,$ sudo./devicequeryInstalling addi

The following is the configuration of vs2005, and vs2003 and vs2008 are similar. 1. Install the Visual Studio 2005 environment. 2. Install the Development Assistant visual assist X. 3. Download Cuda software from the http://www.nvidia.cn/object/cuda_get_cn.html and install it in order. The following is the configuration of vs2005, and vs2003 and vs2008 are similar. 1. Install the Visual Studio 2005 environment.2. Install the Development Assistant vis

1, will c:/documents and Settings/all users/application data/nvidia corporation/nvidia CUDA sdk/doc/syntax_highlighting/ Copy the Usertype.dat file from the visual_studio_8 to the C:/Program files/microsoft Visual Studio 8/common7/ide directory (appended to the original if it already exists). 2. Open the Vs2005-> tools (T), Options (O), Projects and Solutions->vc++ Project Settings->c/c++ file extension (ad

containing a highlight for VS.NET/7 and Vs.net 2005/vs 81, copy the%nvidia GPU Computing sdk%\c\doc\syntax_highlighting\visual_studio_8 under the NVIDIA-written syntax file usertype.dat to%visua Studio Under the%\common7\ide;2. Start Visual Studio, choose Tools > Options > Text editor > File extension, set the extension to CU, select Microsoft Visual C + +, and click OK;3. Restart Visual Studio.　　Adding a compilation ruleUsing

$ sudo apt install nvidia-340OK driver installation Complete, reboot4. Installation Cuda (for 18.04) the installation Cuda needs attention here;We need to choose according to CUDNN, first of all, Cuda can only support 17.04,16.04 ubuntu download installation, but, in fact, a bit like word (high version Word can open the lower version of Word file. ) 18.04 version

Getting started with http://www.cnblogs.com/Fancyboy2004/archive/2009/04/28/1445637.html cuda-GPU hardware architecture Here we will briefly introduce that NVIDIA currently supports Cuda GPU, Which is executing CudaProgram(Basically, its shader unit) architecture. The data here is a combination of the information posted by nvidia and the data provided by NVIDIA in various seminars and school courses. There

Reprinted from http://soft.zdnet.com.cn/software_zone/2009/1127/1527418.shtml 1. software requirements: Cudadriver_2.3_winvista_64_190.38_general Cudatoolkit_2.3_win_64 Cudasdk_2.3_win_64 Vs2008 Uninstall the previously installed SDK, toolkit, and driver before installing the software. If the development platform does not support Cuda graphics, you do not need to install cudadriver_2.3_winvista_64_190.38_general. 　　2. Installation check Run nvcc-V in

Find Visual Studio >> Visual Studio Tools in the Start menuChoose the 86 or 64 version of the VC command prompt environment that I useVS2013 x86 Native Tools Command PromptThis should configure the path of the VC compiler, and the path of the NVCC (cuda C compiler) in the environment variable.And then enterNVCC Cudafilename.cu-o OutfilenameThis format, such asNVC

-level concepts such as Cuda context, just like the context of the host processor, and cuad module, just like the device dynamically loads the library. Because most applications do not require additional level of control, they do not use driver APIs, but use runtime. Context and module management are implicit, the result is that the written code is simple and clear. The driver API is described in the driver API section. The complete description is pro