tesla cuda

During this time, I became familiar with Cuda and added the triangle mesh model for my experiment Renderer, We initially transplanted the original KD-tree to the GPU, but the structure of the KD-tree is still in the CPU. From simple smallpt (all of which are sphere) to the present,ProgramThe structure has been modified several times. Currently We still haven't found a good model. Cuda needs to inline all

Preface: from the previous article "Cuda Programming Interface (ii) ------ 18 weapons" to the present, it has been almost three months, and I don't know how everyone is doing in the "Summer vacation, what have you experienced? I spent two weeks before I went to bed. After reading the fifth book of "those things of the Ming Dynasty", I looked at the weapons of the Ming Dynasty, and thought about the Major of aircraft design I learned. The weapons of th

Login system with username cluster1. Check if the GPUis installed: Lspci | Grep-i nvidia 2. Install gcc,g++ compiler sudo yum install gcc sudo yum install gcc-c++ 3. Installing kernel-devel sudo yum install Kernel-devel 4. installation of Driver,Toolkit and Samples sudo sh cuda_5.5.22_linux_64.run--kernel-source-path= '/usr/src/kernels/2.6.32-358.23.2.el6.x86_64 ' Here we have installed a matching driver, so the first Driver out of the t

Recently need to use matconvnet under Ubuntu16.04. Because TensorFlow 1.6 supports Cuda 9.0, the new machine is loaded directly 9.0 but there are some problems when compiling matconvnet.1. Error using MEX NVCC fatal:unsupported GPU architecture ' compute_20 'Solution: This is because Cuda 8 does not support COMPUTE_20, the lowest is compute_30. So you need to modify the following code in the VL_COMPILENN.MO

Link addr One: Run the programAccording to the previous article, after installing the Cuda software, you can use the "nvcc-v" command to view the compiler version used, I use the version information from: "Cuda compilation tools, Release 3.2, V0.2.1221." Create a directory yourself, in which the new CU file, write code, save, you can use the terminal to switch to the corresponding directory to compile, comp

A very simple Cuda program, suitable for people who have just reached Cuda to understand how Cuda works, and the basic usage of combining with OPENCV. #include http://blog.csdn.net/mmjwung/article/details/6273653

CUDA function Header file __global____device__ #include Threadidx #include #include __SHFL () #include Tex1dfetch () #include Common function header files under "CUDA" Windows

In fact, the basic configuration is really troublesome, and after the configuration is complete, your project folder should be placed in D:/mydoc/Visual Studio 2008/projects, is it because I have not configured the program correctly only in this folder. In short, it is very difficult to configure your own, the specific steps can be found by Google Eldest Brother. Later, difficult afraid of people, and finally found the teacher Kai Yong write cuda_vs_wizard_w32.2.0 (: http://download.csdn.net/

;} Cudaerror_t multicuda (float * C, float * a, float * B, unsigned int ah, unsigned int aw, unsigned int BH, unsigned int BW){Float * gpu_a = 0;Float * gpu_ B = 0;Float * gpu_c = 0;Cudaerror_t cudastatus; Cudastatus = cudasetdevice (0 );If (cudastatus! = Cudasuccess ){Fprintf (stderr, "cudasetdevice failed! Do you have a cuda-capable GPU installed? ");Goto error;}Size_t size_a = ah * Aw * sizeof (float );Cudastatus = cudamalloc (void **) gpu_a, size

To improve cuda efficiency the use of asynchronous functions is a very general choice, but asynchronous functions are not as intelligent as I have imagined.It wants the data that you want to transfer asynchronously on the host side (hosts) cannot be changed, that is, the asynchronous function just indicates the location of a pointer, and does not cache the data, to the real need to go to the host memory to find this value. So when doing asynchronous,

Prior to the introduction of basic CUDA programming knowledge, then this article on the basis of the GPU in processing data calculation of the efficient performance, we take the matrix multiplied as an example.Performs matrix multiplication and performance on 1.CPU.The code for the Matrix multiplication operation on the CPU:mat_mul.cc:wtime.h:wtime.cc:MakefileResults:Performs matrix multiplication and performance on 2.GPU.Code:CUDA_MAT_MUL_V1.CU:cuda_

1. In a CudaProgramBasic hostCodeMainly to complete the following tasks 1) Start cuda, add the device number when using multiple cards, or use cudadevice () to set the GPU device. 2) allocate memory on the CPU and GPU respectively to store input and output data. Remember to initialize the data on the CPU and then swap the data into the memory. 3) Call the kernel program on the device side for computation, write the result to the relevant area of th

Labels: use Windows to program the user's computer memoryGPUs has a display output that is output to a DRAM Memory called the main surface, which is used to refresh the display device output to the user. When you start a display mode selection by changing the resolution or depth of the display (using the NVIDIA control panel or Windows Display Control Panel), the amount of memory required for the main surface changes. For example, if you change the display resolution from 1280x1024x32 bit to 160

CUDA program Run CPU 100% problem is a bit of a headache, in the experimental process called the kernel function, and then call Cudamemcpyasync, but now there will be a block in this so-called Async api,strace followed a bit, Found that 99.999% were allClock_gettime (Clock_monotonic_raw, {2461, 485666623}) = 0So there's an inspiration, why don't I write a similar poll function, but I'm polling every 1 minutes, so I can drop the CPU usage. kernelvoi

Caffe is an efficient, deep learning framework. It can be executed either on the CPU or on the GPU.The following is an introduction to the Caffe configuration compilation process on Ubuntu without Cuda:1. Install the blas:$ sudo apt-get install Libatlas-base-dev2. Install dependencies: $ sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5- Serial-dev Protobuf-compiler Liblmdb-dev3. Install Glog (dow

Refer to StackOverflow a post processing method: Https://stackoverflow.com/questions/26913683/different-way-to-index-threads-in-cuda-cThe Cuda_gridsize function in the code references YOLO.The code is as follows:#include"cuda_runtime.h"#include"Device_launch_parameters.h"#include#include#include#includeusing namespacestd;#defineBLOCK 512dim3 cuda_gridsize (size_t N) {size_t k= (N-1)/BLOCK +1; unsignedintx =K; unsignedinty =1; if(X >65535) {x=Ceil (sqr

I wrote two articles before. One is the C + + serial implementation of the KNN algorithm, and the other is the Euclidean distance of the Cuda computational vector. Then this article can be said to be a simple integration of the first two articles. You can read the first two articles before reading this article.First, generate a data setNow we need to generate a n d-dimensional data, not a group of data have a class label, this class is labeled accordi

After the Cuda is installed, you can use Devicequery to look at the related properties of the GPU, so that you have a certain understanding of the GPU, which will help cuda programming in the future. #include "cuda_runtime.h" #include "device_launch_parameters.h" #include The number of Nvidia GPU in the system is first obtained by Cudagetdevicecount , and then the properties of the GPU in the system ar

NVIDIA NVCC compiler driver converts. cu files to c for binary instructions for host systems and CUDA assemblies or devices. It supports a number of command-line parameters, with the following being particularly useful for optimization and related best practices:‣-maxrregcount = n Specifies the maximum number of registers that the kernel can use at each file level. See registration pressure. (See also the "Performing Configuration" in the

, indeed is a period of time again think of, since called GPU Revolution, that must gather the team Ah, I began to recruiting. Business: In order to get into the Cuda parallel development, we must understand the Cuda's running model before we can develop the parallel program on this basis. Cuda is executed by letting one of the host's kernel perform on the graphics hardware (GPU) according to the concept