cuda deep learning

Caffe (convolution Architecture for Feature Extraction) as a very hot framework for deep learning CNN, for Beginners, Build Linux under the Caffe platform is a key step in learning deep learning, its process is more cumbersome, recalled the original toss of those days, then

original articles, reproduced please indicate the source ... I. Background of the problem Recently to do a learning sharing report on Cuda, I would like to make an example of using Cuda for image processing in the report, and use shared memory to avoid the global memory not merging, improve image processing performance. But for the

Fri., April, 2014, Issue #110 Read Newsletter Online | Previous Issues Welcome to Cuda:week in ReviewNews and resources for the worldwide GPU and parallel programming community. CUDA PRO TIP CUDA 6 XT Librar

Nvidia DIGITS Learning Notes (nvidia DIGITS-2.0 + Ubuntu 14.04 + CUDA 7.0 + CuDNN 7.0 + Caffe 0.13.0)Enjoyyl 2015-09-02 machine learning original linkNVIDIA DIGITS-2.0 + Ubuntu 14.04 + CUDA 7.0 + CuDNN 7.0 + Caffe 0.13.0 Environment configuration Introduction Digits Introduction Digits ch

NVIDIA DIGITS-2.0 + Ubuntu 14.04 + CUDA 7.0 + CuDNN 7.0 + Caffe 0.13.0 Environment configuration Introduction Digits Introduction Digits characteristics Resource information Description Digits installation Hardware and Software Environment Hardware environment Software Environment Operating system Installation Digits Pre-Installation preparation

;}//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////Copy input vectors from the host memory to GPU buffers.Copy A's data from the CPU to the GPUCudastatus = cudamemcpy (Dev_a, A, TOTALN * sizeof (int), cudamemcpyhosttodevice);if (cudastatus! = cudasuccess) {fprintf (stderr, "cudamemcpy failed!");Got

, first of all to register the NVIDIA Development Account, then can download CUDNN.To put it simply, a few files are copied: library files and header files. Copy the CUDNN header file to/usr/local/cuda/lib64 and copy the CUDNN library file to/usr/local/cuda/include.After downloading the CD into the file package directory, unzip the file:TAR-ZXF cudnn-7.0-linux-x64-v4. 0-prod.tgzcd

combined feature extraction system capabilities, the computer vision, speech recognition and natural language processing and other key areas to achieve a significant performance breakthrough. The study of these data-driven technologies, known as deep learning, is now being watched by two key groups in the technology community: The researchers who want to use and train these models for extreme high-performa

, etc. ) constitute a SM.(4) Warp:gpu the dispatch unit when executing the program. Currently Cuda's warp size is 32, same as in a warp thread, executing the same instruction with different data resources.6. Cuda kernel functionThe complete execution configuration parameter form of the kernel function is (1) Parameter DG is used to define the dimensions and dimensions of the entire grid, that is, how many blocks a grid has.(2) Parameter DB defines the

, because at one time it comes faster and the GPU prefers bus because of the throughput.What's Q:cuda? CUDA programming Software-level structure? A:Q:cuda Programming Note what?A: Notice what the GPU is good at!-Efficiency launching lots of threads-Running lots of threads in parallelIs there a limit to the parameters when declaring Q:kernel?A:We studied in the CUDA series (i) an Introduction to GPU and

Cuda time is not long, the first is in the Cuda-convnet code to contact Cuda code, it did look more painful. Recently Hollow, in the library borrowed this "GPU high-performance programming Cuda combat" to see, but also organize some blogs to enhance learning effect.Jeremy Li

1. Two-D arrays using1#include 2#include 3 using namespacestd;4 5 Static Const intROW =Ten;6 Static Const intCOL =5;7 8 intMain () {9 int* * Array = (int**)malloc(row*sizeof(int*));Ten int* Data = (int*)malloc(row*col*sizeof(int)); One A //Initialize the data - for(intI=0; i) { -Data[i] =i; the } - - //Initialize the array - for(intI=0; i) { +Array[i] = data + i*COL; - } + A //Output the array at for(intI=0; i) - for(intj=0; j)

Cuda Basic Concept Cuda grid limits 1.2CPU and GPU design differences 2.1cuda-thread2.2cuda-memory (storage) and Bank-conflict2.3cuda matrix multiplication 3.1 Global storage bandwidth and consolidated access Memory (DRAM) bandwidth and memory coalesce3.2 convolution 3.3 analysis of the multiplexed 4.1Reduction model of convolution multiplication optimization 4.2 CUDA