ubuntu cuda

Cuda-convnet is a set of CNN code published by Alex Krizhevsky , running on a Linux system, using the GPU to perform operations, providing only a demo of the CIFAR data set in Cuda-convnet. And the website does not explain how the Cuda-convnet code is used in other databases, so Bo Master I try to modify the source, for the mnist data set, to do a handwritten num

In recent days want to c,cuda,mpi mixed compiled Linux to rewrite the dynamic link library libtest.so, after two or three days of the first large variety of search information, turn over a variety of makefile files, all kinds of reading blog, finally. Finally, I'm crying for joy. 1. First understand how the CPU side to encapsulate code into a dynamic link library Reprint Address: http://www.cnblogs.com/huangxinzhen/p/4047051.html Of course, a lot of r

Just like a freshman C ++ or a sophomore compilation, I also wrote Cuda for a few months. Then, think about it, and I should start to explain it, I learned something at the lower layer of Cuda and may know more about heterogeneous programming. 1 OverviewFull name of opencl: Development Computing language, parallelProgramThe development standard, used in combination with any heterogeneous platform-includin

exchange of ideas. In fact, when learning engineering, there is a little trick, that is to find the rules. There are established rules, and that is the theorem and the definition. If you can find a new rule, it is a new discovery that can be written paper. When we meet new things, we'd better find the shadow in our own thinking and find the same rules. So that you can learn new things very well. However, often learn engineering thinking more regular, in addition to the usual reading of the engi

CUDA Driver API Usage notes1. IntroductionThe Cuda Driver API is implemented in the Cuda Dynamic Library (libcuda.so). If you are developing in an eclipse environment, you need to add the path to the libcuda.so file and reference the Cuda.h file in your program.2. Environment configuration2.1 Source ProgramFor the use of the driver API simply include the correspo

Everybody put Gpucuda said very NB malicious NB, so, next want to run through the GPU Acceleration program. This one weeks, all in the configuration OpenCV cuda environment, today finally ended in failure, because the lab's machine graphics do not support cuda ... Can't hurt, a week AH!!!CUDA-enabled Gpu:http://developer.nvidia.com/

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)

people who understand the JPEG data format should be able to imagine that the method of splitting and compressing images with 8*8 pixel block size is very easy to implement with parallel processing ideas. In fact, Nvidia's Cuda has provided examples of JPEG codecs since v5.5. The example is stored in the Cuda SDK, the default installation path for Cuda "C:\Progra

The premise is that the computer graphics card support Cuda,n card is generally supported, if it is a card will not be able to.Primarily for Windows environments, Linux and Macs also have corresponding installation packages.CUDA Environment Construction:STEP1: Install code environment VS2010;STEP2: Update nvidia driver;STEP3: Installing CUDA Toolkit;STEP3: Installing the GPU Computing SDK;STEP1~STEP3 relate

Sometimes it is necessary to do coding work through Remote Desktop Connection, such as the general web, such as the need for the GPU and other support coding work directly with Windows Remote Desktop Connection coding and then debug, and some need to rely on graphics support work such as rendering, When GPU operations such as CUDA, Remote Desktop Connection debug will fail. Because when using Remote Desktop to connect computer B, such as the original

CUDA, cudagpuMemory The level of kernel performance cannot be simply explained from the execution of warp. As mentioned in the previous blog post, setting the block dimension to half the warp Size will reduce the load efficiency, which cannot be explained by the scheduling or parallelism of warp. The root cause is the poor way to get global memory. As we all know, memory operations play a very important role in efficiency-oriented languages. Low-laten

I just read something about Cuda and planned to write a program. As a result, I encountered a bunch of problems. The first problem is the array transfer problem on the host and device, which is a bit dizzy. After reading some information, I will summarize it as follows. 1: How did the problem come about? One-dimensional array, two-dimensional array, and three-dimensional array are used on device. For one-dimensional arrays, cudamalloc and cudamemcpy a

This section describes the main concepts of the Cuda programming model. 2.1.kernels (kernel function) Cuda C extends the C language and allows programmers to define C functions, called kernels ). Execute n times in N Cuda threads in parallel. Use the _ global _ specifier to declare a core function, call and use For example, add two vectors, add a and B, and stor

Http://blog.csdn.net/yutianzuijin/article/details/8147912category: Programming Language 2521 people read comments (0) Add to favorites report cudagpu Recently, I first tried Cuda programming. As a newbie, I encountered various problems and spent a lot of time solving these incredible problems. In order to avoid people from repeating the same mistakes, we will summarize the problems we have encountered as follows. (1). cudamalloc The first time I used

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