cuda tools

Now it is necessary to get the sum of all the elements of an array, which seems unlikely before, because each thread only processes one element and cannot relate all the elements, but has recently learned a piece of code that can be implemented, and also has a further understanding of shared memory.First, C + + serial implementationThe method of serial implementation is very simple, as long as all elements are added sequentially to get the corresponding results, in fact, we focus on not the resu

1. Install Blas:$ sudo apt-get install Libatlas-base-dev2. Install the dependencies:$ sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev Libhdf5-serial-dev Protobuf-compiler Liblmdb-dev3. Install additional dependencies:$ sudo apt-get install Libgflags-dev libgoogle-glog-dev Liblmdb-dev4. Download Caffe:$ git clone git://github.com/bvlc/caffe.gitBecause of the slow download speed, this step can be directly used by others to download the Caffe package

CUDA cannot recognize texture Just began to learn cuda texture memory, from the Internet to find learning materials, but the test, the program is prompted error: Texture Output[y*width + x] = tex2d (Texref, TU, TV); Texture,tex2d not recognized The first thought was to find the definition of the function, which was defined in the Cuda_texture_types.h file, defined as Template , so the hea

GPGPU is a nuclear equipment, including a large number of computing units, to achieve ultra-high speed parallelism. When you use Cuda to program on the NVIDIA graphics card, you can use the event provided by Cuda to do the timer. Of course, each programming language basically provides a function to get the system time, such as the C/c++/java program timer function An event can be used to count the exact

In the fifth lecture, we studied the GPU three important basic parallel algorithms: Reduce, Scan and histogram, and analyzed its function and serial parallel implementation method. In the sixth lecture, this paper takes the Bubble sort, merge sort, and sort in the sorting network, and Bitonic sort as an example, explains how to convert the serial parallel sorting method from the data structure class to the parallel sort, and attach the GPU implementation code.In the parallel method, we will cons

Original article link Today, we will introduce the relevant properties of Cuda devices. We can write code that is more suitable for hardware work only when we are familiar with the hardware and how it works. The cudadeviceprop struct records the properties of the device. 1 struct cudadeviceprop 2 {3 char name [256];/** Use cudagetdeviceproperties () to obtain the device attribute. Use cudagetdevicecount () to obtain the number of devices. Use cudacho

Source code and running result Cuda: https://github.com/zhxfl/cuCNN-I C language version reference from: http://eric-yuan.me/ The accuracy of the mnist library for famous handwritten numbers recognition is 99.7%. In a few minutes, CNN training can reach 99.60% accuracy. Parameter configuration The network configuration uses config.txt for configuration # comments between them, and the code will be filtered out automatically. For other formats, refer

1. when compile /home/wangxiao/NVIDIA-CUDA-7.5 SAMPLES, it warning: gcc version larger than 4.9 not supported, so:old verson of gcc and g++ are needed: sudo apt-get install gcc-4.7 sudo apt-get install g++-4.7 Then, a link needed:sudo ln-S/Usr/Bin/gcc-4.7 / usr/local/cuda/bin/gccsudo ln - s /usr/bin /g++-4.7/usr/local/ cuda/bin/g ++ When c

(texref1d));//Unbind -Cutilsafecall (Cudafree (dev1d));//Free memory Space $ Cutilsafecall (Cudafree (DEVRET1D)); theFree (HOST1D);//free up memory space the Free (HOSTRET1D); the the ///2D Texture Memory -cout "2D Texture"Endl; in intwidth =5, height =3; the float*HOST2D = (float*) Calloc (width*height,sizeof(float));//Memory Raw Data the float*HOSTRET2D = (float*) Calloc (width*height,sizeof(float));//Memory return Data About theCudaarray *cuarray;//

There are two versions that developers need to care about when developing Cuda applications: computing capability-describe product specifications and computing device features and Cuda driver API version-Describe the features supported by the driver API and runtime.You can obtain the driver API version from the macro cuda_version in the driver header file. Developers can check whether their applications req

Cuda file organization Original article address:Cuda Study Notes 2 Author:Ye Isaac Cuda file organization: 1. Cuda projects can contain. Cu AND. cpp. 2. In the. Cu file, you can use # include "cuda_x.cuh" to call the functions in. Cu or # include "cpp_x.h ". For example, declare Class A in test1.h; Define the related member functions of Class A in t

I have long heard of many advantages of Cuda nexus: Support for GPU thread debugging and analysis... It took me one afternoon to build the Cuda nexus environment. The following are the points to pay attention to when building: I. Hardware: During remote debugging, the target machine's video card must be a Cuda Device of G92 or gt200, and the host can be any vi

Deep learning is an important tool for the study of computer vision, especially in the field of image classification and recognition, which has epoch-making significance. Now there are many deep learning frameworks, and Caffe is one of the more common ones. This article describes the basic steps for configuring Caffe in the Ubuntu 14.04 (64-bit) system, referring to the official website of Caffe http://caffe.berkeleyvision.org/.First, the system environment configuration1.1 First install some de

Compiling the environmentVMWare Workstation PlayerUbuntu-15.10-desktop-amd64CPU 4700MQ, allocating 6 cores +4GB memory +80GB HDD to VMCompile stepThe main reference is Caffe official websiteHttp://caffe.berkeleyvision.org/install_apt.html1. Install the Basic Package sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev Protobuf-compilersudo apt-get install--no-install-recommends Libboost-all-dev Cuda