enable cuda

Link addr One: Run the programAccording to the previous article, after installing the Cuda software, you can use the "nvcc-v" command to view the compiler version used, I use the version information from: "Cuda compilation tools, Release 3.2, V0.2.1221." Create a directory yourself, in which the new CU file, write code, save, you can use the terminal to switch to the corresponding directory to compile, comp

A very simple Cuda program, suitable for people who have just reached Cuda to understand how Cuda works, and the basic usage of combining with OPENCV. #include http://blog.csdn.net/mmjwung/article/details/6273653

We all say that GPU Cuda is very Nb-hard, so the next step is to run the program through GPU acceleration. This week, we have been configuring the Cuda environment of opencv. Today we finally ended up failing because the graphics card of the lab machine does not support Cuda... Sorry, a week !!! Cuda-supported GPU: h

Transferred from: http://www.cnblogs.com/yeahgis/p/3853420.htmlVS2010 Error: The imported project XXX was not found, make sure that the path in the E:\igsnrr\dev\phdthesiscode_cuda\gtcg\gtcg.vcxproj:error: Imported items not found "C:\Program Files (x86) \msbuild\microsoft.cpp\ V4.0\buildcustomizations\cuda 5.5.props ". Make sure that the path in the Workaround: Locate the project Engineering Vcxproj file and find all "

Flow: The Cuda stream is much like a CPU thread, and the operations in a cuda stream are sequential and coarse-grained to manage the concurrent execution of multiple processing units.In layman's terms, the flow is used for parallel operations, such as processing the same image, you use a stream to process the left half of the picture, and then the second stream to process the right half of the picture, the

1. First For cuda8.0, cuda7.5 Uninstall can be compatible After installing cuda9.0, the original NVIDIA graphics driver for the computer will be updated and the Nvidia PhysX system software will be updated (installing the low cuda may not be updated). Please pay attention when unloading, do not move these 2. 2. Uninstall:1. Preface: Anti-virus software to uninstall this, not easy to find. Open the computer's control pane

This article installs the environment:-Dual Graphics: Intel set + NVIDIA single display-Ubuntu 14.04.4-CUDA 8.0.441. The Deb installation package is a pit ( don't use this method!) )With the DEB installation package Cuda-repo-ubuntu1404-8-0-local_8.0.44-1_amd64.deb, after the installation is complete, the reboot appears with a black screen,-resolution after a black screen appears:(1) Ctrl + Alt + F1 into co

First build your own project, ordinary C + + project Write the C + + program you want to run Check a cuda option, because I installed two, casually select one can Select Properties Of course, if the new time is based on cudac++ then there is no need to choose this step. Then open the properties of the project and add Cudart.lib to the attachment dependencies in the configuration Properties-linker-Input And then you can write your own code. The code

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

cudaevent_t Start,stop;Cudaeventcreate (start);//Create EventCudaeventcreate (stop);Cudaeventrecord (start,0);//Record Current timeThings to keep track of time/workCudaeventrecord (stop,0);//Record Current timeCudaeventsynchronize ();//Syncfloat ElapsedTime;Cudaeventelapsedtime (elapsedtime,start,stop);//calculation of the time difference, that is, the execution times of the eventCudaeventdestroy (start);//Destroy EventCudaeventdestroy (stop);The Cuda

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

This article refers to self-http://blog.163.com/yuhua_kui/blog/static/9679964420146183211348/Problem Description:When running the CUDA program, a black screen appears, after a while screen recovery, the following interface appears:==============================================================================Solution: Adjust the TDR value of the computer Timeout Detection Recovery (TDR)TDR Official explanation Document Link: http://http.developer.nvid

Data transmission test: first transmitted from the host to the device, then transmitted within the device, and then from the device to the host. H --> d D --> d D --> H 1 // movearrays. cu 2 // 3 // demonstrates Cuda interface to data allocation on device (GPU) 4 // and data movement between host (CPU) and device. 5 6 7 # include Test environment: Win7 + vs2013 + cuda6.5 Download link GPU Cuda: data trans

Array in reverse order, the array initialized on the host is transmitted to the device, and then the Cuda parallel reverse order is used. At this time, the operation is performed on the global memory, and then the result is returned to the host for verification. 1 # include Cuda implements array Reverse Order

addition, the algorithm has an important performance improvement compared with the light tracing algorithm on the traditional CPU. By testing the rendering time, the following Algorithm Execution and acceleration ratio are obtained through statistics: Table 1. Time list obtained by performing rendering tests on several typical scenarios. The GPU platform used in the test is GTX 260. From the above table, we can see that the algorithm is properly parallel and then transplanted to the GPU platfo

The Cuda model is very concise. It basically calls functions for Parallel Processing for a large segment of data.However, there are many restrictions currently. For example, all functions executed on the GPU must be inline, which means you cannotUse modular or object-oriented design to separate complex systems. There are also very limited registers,It is basically not enough for ray tracing, which makes the GPU throughput not high.However, as a rapidl

1. GPU is superior to CPU in terms of processing capability and storage bandwidth. This is because the GPU chip has more area (that is, more transistors) for computing and storage, instead of control (complex control unit and cache ). 2. command-level parallel --> thread-level parallel --> processor-level parallel --> node-Level Parallel 3. command-level parallel methods: excessive execution, out-of-order execution, ultra-flow, ultra-long command words, SIMD, and branch prediction. Ultra-long sc

of glupostredisplay () in idle and idle, the program returns to display again. At this time, the timer at the start of display record the current moment again. The difference between the two moments is the time used to render a frame. When the FPS is very high, you can accumulate time to obtain accurate FPS. For example, a frame may only be 0.000001 ms, but the time of 100000 frames is relatively large. In addition, the gluswapbuffers will call glfinish () implicitly, which is used in the

The GPU module of opencv provides many parallel functions implemented by cuda, but sometimes you need to write parallel functions and use them with existing opencv functions. opencv is an open-source function library, we can easily see its internal implementation mechanism, and write a Cuda parallel function based on its existing functions. The key GPU classes are gpumat and ptrstepsz. Gpumat is mainly use