cuda in python

Caffegit clone git://github.com/bvlc/caffe.git7. Installing CaffeCP Makefile.config.example Makefile.configBecause there is no GPU here, you need to set cpu_only:= 1 in the Makefile.config file to remove the comment.and then compile Make All Make Test make RuntestAfter installation we can try to run a lenet on the mnist.1. Get Mnist Data firstCD Caffe. /data/mnist/get_mnist. SH2. Then create the lenet, be sure to run the following command at the root of the Caffe, otherwise the "Build/exampl

In some applications we need to implement functions such as linear solvers, nonlinear optimizations, matrix analysis, and linear algebra in the GPU. The Cuda library provides a Blas linear algebra library, Cublas. BLAS specifies a series of low-level lines that run common linear algebra operations, such as vector addition, constant multiplication, inner product, linear transformation, matrix multiplication, and so on. Blas has prepared a standard low-

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

OPENCV's GPU module provides many parallel functions for CUDA implementations, but sometimes it is necessary to write parallel functions and use them in conjunction with existing OPENCV functions, while OpenCV is an open-source library of functions, and we can easily see the implementation mechanism within it. You can write one of your own Cuda parallel functions based on his existing function Bishi. The k

designed to take into account the program execution and data operations parallelism, versatility and their balance. The micro-architecture of GPU is designed for the numerical calculation of matrix type, which is a large number of iterative design of computational units, which can be divided into many independent numerical calculations-a large number of numerical operations of the thread, and the data is not like the logic of the implementation of the logical correlation.However, after all, Mic

First, preface This article is a summary of some basic knowledge of learning Cuda programming process, referring to the GPU high performance programming combat second, query the properties supported by the graphics card attribute queries, it is important to know the number of CUDA processors supported by the graphics card, the compute power, the maximum number of thread blocks that each dimension can contai

This program is to add two vectorsAddTid=blockidx.x;//blockidx is a built-in variable, blockidx.x represents this is a 2-D indexCode:/*============================================================================Name:vectorsum-cuda.cuAuthor:canVersion:Copyright:your Copyright NoticeDescription:cuda Compute reciprocals============================================================================*/#include using namespace Std;#define N 10__global__ void Add (int *a,int *b,int *c);static void Checkcud