cuda tools

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

Introduction to Ubuntu 16.04 Development Cuda Program (i)Environment: Ubuntu 16.04+nvidia-smi 378.13+cmake 3.5.1+cuda 8.0+kdevelop 4.7.3 Environment ConfigurationNvidia driver, CMake, Cuda configuration method See: Ubuntu 16.04 Configuration Run kintinuous kdevelop configuration: command line input sudo apt-get install Reference DocumentsLiu Jinxian and so on. Pa

1, first need to uninstall the system comes with the NVIDIA-related driver: $ sudo apt-get –purge remove nvidia-GLX Nvidia-GLX-New$ sudo apt - Get –purge remove nvidia-settings nvidia-kernel -Common 2, after the original drive deleted, also need to add Ubuntu integrated open source driver blacklist, that is, modify the/etc/modprobe.d/blacklist.conf file: sudo gedit /etc/modprobe. D / blacklist . confAdd the following two lines to the file:Blacklist nouveauOptions Nouveau modeset=03, in

The content of further learning after getting started is how to optimize your code. Our previous example did not consider any performance optimizations in order to better learn the basic points of knowledge, rather than other detail issues. Starting with this section, we want to think about performance and constantly optimize the code, making execution faster is the only purpose of parallel processing. There are many ways to run the code, and the C language provides an API similar to SYSTEMTIME

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