cuda deep learning

Discover cuda deep learning, include the articles, news, trends, analysis and practical advice about cuda deep learning on alibabacloud.com

Deep Learning Learning Summary (i)--caffe Ubuntu14.04 CUDA 6.5 Configuration

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

CUDA Learning notes One: CUDA+OPENCV image transpose, using shared memory for CUDA program optimization

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

NVIDIA Update:cuda Week in Review (Spotlight on Deep neural; CUDA 6)

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)

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 Learning Notes (nvidia DIGITS-2.0 + Ubuntu 14.04 + CUDA 7.0 + CuDNN 7.0 + Caffe 0.13.0)

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

Cuda Learning: First CUDA code: Array summation

;}//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////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

Ubuntu Configuration Machine learning Environment (ii) CUDA and CUDNN installation

, 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

Deep learning FPGA Implementation Basics 0 (FPGA defeats GPU and GPP, becoming the future of deep learning?) )

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

Cuda Learning and Summary 1

, 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

Cuda Series Learning (iii) GPU design and Structure QA & coding Exercises

, 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 Learning log: Thread collaboration and routines

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

Cuda Learning and Summary 2

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 Advanced Learning

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

Machine Learning Environment configuration Series 1 Cuda

The environment configured in this article is redhat6.9 + cuda10.0 + cudnn7.3.1 + anaonda6.7 + theano1.0.0 + keras2.2.0 + jupyter remote, with Cuda version 10.0. Step 1: before installing Cuda: 1. Verify if GPU is installed $ Lspci | grep-I NVIDIA 2. Check the RedHat version. $ Uname-M CAT/etc/* release 3. After the test is completed, download Cuda from the

Cuda Learning: Further understanding of blocks, threads

(8,8);Launch a kernel on the GPU with one thread for each element.Threads that start each cell on the GPUSumarrayCudadevicesynchronize waits for the kernel to finish, and returnsAny errors encountered during the launch.Wait for all threads to run endCudastatus = Cudadevicesynchronize ();if (cudastatus! = cudasuccess) {fprintf (stderr, "Cudadevicesynchronize returned error code%d after launching Addkernel!\n", cudastatus);Goto Error;}Copy output vector from the GPU buffer to host memory.Cudastat

Cuda learning ing.

0. IntroductionThis paper records the learning process of cuda-just beginning to touch the GPU-related things, including graphics, computing, parallel processing mode, first from the concept of things to start, and then combined with practice began to learn. Cuda feel no authoritative books, development tools change is faster, so the total feeling is not very pra

Cuda learning from the CPU architecture

Recently to learn GPU programming, go to the NVIDIA network download Cuda, the first problem encountered is the choice of architectureSo the first step I learned was to learn about the CPU architecture, x86-64 abbreviated x64, a 64-bit version of the x86 instruction set, forward-compatible with the 16-bit version and the 32-bit version of the x86 architecture. x64 was originally designed by AMD in 1999, and AMD first exposes 64-bit sets to x86, called

Cuda Programming Learning 3--vectorsum

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

Cuda Programming Learning 5--Ripple Ripple

char) (128.0f+127.0f*cos (d/10.0f-ticks/7.0f)/(d/10.0f+1.0f));Ptr[offset*4+0]=grey;Ptr[offset*4+1]=grey;Ptr[offset*4+2]=grey;ptr[offset*4+3]=255;}int main (){DataBlock data;Cpuanimbitmap bitmap (Dim,dim,data);Data.bitmap = bitmap;Cuda_check_return (Cudamalloc (void * *) data.dev_bitmap,bitmap.image_size ()));Bitmap.anim_and_exit ((Void (*) (Void*,int)) Generate_frame, (Void (*) (void*)) cleanup);}void Generate_frame (DataBlock *d,int ticks){A total of dimxdim pixels, each pixel corresponding to

Cuda Learning Note Two

The simple vector Plus/** * Vector addition:c = a + B. * * This sample was A very basic sample that implements element by element * Vector Addit Ion. It is the same as the sample illustrating Chapter 2 * of the Programming Guide with some additions like error checking. */#include Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced. Cuda Learning Note T

Total Pages: 15 1 2 3 4 5 .... 15 Go to: Go

Contact Us

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion; products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the content of the page makes you feel confusing, please write us an email, we will handle the problem within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to: info-contact@alibabacloud.com and provide relevant evidence. A staff member will contact you within 5 working days.

A Free Trial That Lets You Build Big!

Start building with 50+ products and up to 12 months usage for Elastic Compute Service

  • Sales Support

    1 on 1 presale consultation

  • After-Sales Support

    24/7 Technical Support 6 Free Tickets per Quarter Faster Response

  • Alibaba Cloud offers highly flexible support services tailored to meet your exact needs.