cuda fortran

Original works, reproduced please specify the source: http://www.cnblogs.com/shrimp-can/p/5253672.html1. Viewing toolsThe default directory is: local, enter local:cd/usr/localInput command: LS, view the files in this directory, you can see the installation of Cuda hereEnter Cuda file: CD cuda-7.5 (mine is 7.5), here for the installation of somethingLocate the ins

In addition to writing Cuda code directly in a project using CU or Cuh, you can place the Cuda related action code in a DLL project, compile the project into a dynamic-link library dll, and then refer to the DLL in the project you want to use and call its internal functions. Now create a new DLL project with the project name Test00302, as shown in the following illustration: Now create a new file named Te

support for NVIDIA libraries and using the resulting binaries to speed up video Encodin G/decoding. FFmpeg supports following functionality accelerated by video hardware on NVIDIA gpus:hardware-accelerated encoding of H.2 hardware-accelerated decoding** of H. hevc*, HEVC, VP9, VP8, MPEG2, and mpeg4* granular control over encoding SE Ttings such as encoding preset, rate control and other video quality parameters Create high-performance end-to-end Hardwar e-accelerated video processing, 1:n encod

In order to learn deep learning, these days in the installation of deep learning framework, CUDA installation is not able to locate the package problem. CUDA official website is available in the Deb and run format, today only the Deb format installation package installation process issues.Following the official tutorial, download the Cuda deb package and usesudo

CUDA, cudagpuDynamic Parallelism So far, all the kernel is called on the host, and the GPU works completely under the control of the CPU. CUDA Dynamic Parallelism allows the GPU kernel to create a call on the device. Dynamic Parallelism makes recursion easier to implement and understand, because the startup configuration can be determined by the thread on the device at runtime, which also reduces data trans

Today, using the thread in Cuda block to modify the hexahedral of two for loops has been wrong.// For (int i= 0;i// { int j= threadidx.x; int i= threadidx.y; // For (int j = 0; j {Is that the incorrect sequence of I and J affects the coordinate position when calculating vertex coordinates: Boxverticescuda[gridindexnumstop]. Pos,-boxlength/2+widthblock*j, Boxtopy, (Boxlength/2-lengthblock*i));The Thread.x Thread.y

Create a Cuda project on vs2008, create the test. Cu file, copy the following code, compile and execute the code, and clearly see the difference between GPU running matrix multiplication and CPU efficiency. The following result is displayed on my PC. The GPU efficiency of matrix multiplication is improved by about an order of magnitude (relative to the CPU). The development environment is vs2008 + cuda5.x Development Kit + gt520m graphics card. Progr

Vs2010 error: the imported project XXX is not found. Make sure that the path in the E: \ igsnrr \ Dev \ phdthesiscode_cuda \ gtcg. vcxproj: error: the imported project "C: \ Program Files (x86) \ msbuild \ Microsoft. CPP \ v4.0 \ buildcustomizations \ Cuda 5.5.props ". Make sure that the path in the Solution: Find the vcxproj file of the project and find all "Cuda 5.5" content, as shown below:Modify the ver

Oracle VM VirtualBox Downloadubuntu14.04Install the VirtualBox first and then mount the ubuntu14.04 on top. Note To install the enhancements (after starting the virtual machine, select the "Devices" menu, select the "Insert Guest additions CD Images" option.) If you do not see devices, press the right crtl+c), otherwise the screen is not displayed completely.Caffe installation (temporarily not well, encountered problems: After the installation of Cuda

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

Some time ago, the OPENCV3.4,TX2 update source failed to install the TX2, OPENCV internal many functions have implemented GPU acceleration, but we manually write the function, want to through the GPU acceleration will need to manually call Cuda for acceleration. The following describes Cuda's environment configuration and compilation, respectively, from the Windows platform and the Linux platform.1 Windows VS2013 +

Cuda Memory Model: GPU chip: Register, shared memory; Onboard memory: local memory, constant memory, texture memory, texture memory, global memory; Host memory: host memory, pinned memory. Register: extremely low access latency; Basic Unit: register file (32bit/each) Computing power 1.0/1.1 hardware: 8192/Sm; Computing power 1.2/1.3 hardware: 16384/Sm; The register occupied by each thread is limited. Do not assign too many private variables to it dur

In other words, I have really paid a lot for configuring the Cuda environment: My hardware configuration: Lenovo v460 laptop (the video card is geforce 310 m) Required software: All the software versions I use work with cuda4.0 Cudatoolkit cudasdk nsight vs2008 1. Software Download Download the above software on the official website: The names of the downloaded software are listed below, which are provided for reference to prevent download errors: 1

Here's a small piece to bring you a Python program using the method of writing Cuda. Small series feel very good, now share to everyone, also for everyone to make a reference. Let's take a look at it with a little knitting. There are two ways to use Python to write Cuda programs: * Numba* Pycuda Numbapro is deprecated now, features are split and integrated into accelerate and Numba, respectively. Example N

In recent days, we have been exposed to deep learning, in view of the requirements of deep learning for speed and GPU computing, and the increasing complexity of the derivation calculation after the network layer deepens, the intention is to build a Theano platform (discard matlab), only for your own entertainment (fancy irrigation) ... Main steps: CPU Calculation of Theano Build Cuda VS2010 GPU Computing of Theano 1.Theano CPU

Cuda basics (1): operational procedures and kernel concepts, cudakernel Cuda is a parallel computing framework released by Nvidia. GPU is no longer limited to processing graphics and images. It contains a large number of computing units to execute tasks that are large in computing but can be processed in parallel. Cuda operations include five steps: 1. Memory al

Ubuntu 14.04 LTS is out, loads of new features has been added. Here is some procedures I followed to the install CUDA 6.0 on my DELL Inspiron.First of all, Ubuntu need to be installed successfully, and Thenecessary Libs is also need to installed:sudo apt-get install build-essential gcc-4.4 g++-4.4 libxi-dev libxmu-dev Freeglut3-devThings need to the before start the installationprocess:1. Latest NVIDIA Graphic Driver (nvidia-linux-x86_64-331.49.run)2.

First of all, my computer is Win7 64-bit operating system, installed is 32 for the Vs2008, to compile the OpenCv2.4.3 and TBB4.2; The process of compiling is a lot of people have said the blog of one of the most detailed, also most comprehensive: http://blog.csdn.net/shuxiao9058/article/details/7529684 The following content I just paste this article, easy to see ( where the green part is different from the original text, but also we need to pay attention to): Earlier, we talked about how to u

Recommended New Installation Tutorials http://blog.csdn.net/chenhaifeng2016/article/details/78874883 The install Depth Learning framework requires the use of CUDA/CUDNN (GPU) to speed up computing, while installing CUDA/CUDNN requires Nvidia's graphics driver to be installed first. I ran into a driver conflict during the installation, looping through the two issues so that I finally had to reinstall the o