fortran gpu

= TrueAdded to the file.Fourth Step: Install NVCCThis is easier.sudo apt-get insatll NVCCYou can do it.At this point, all the setup programs are complete.You can use this code to test whether your program uses CPU or GPUFrom Theano import function, config, shared, sandbox import theano.tensor as T import numpy Import Timevlen = Ten * 768 # x #cores x # Threads per core iters = 1000rng = numpy.random.RandomState x = Shared (num Py.asarray (Rng.rand (Vlen), config.floatx)) f = function ([]

1The first thing to do is to turn on GPU acceleration to install CUDA. To install CUDA, first install Nvidia drive. Ubuntu has its own open source driver, first to disable Nouveau. Note here that the virtual machine cannot install Ubuntu drivers. VMware under the video card is just a simulated video card, if you install Cuda, will be stuck in the Ubuntu graphics interface can not log on the system. So first we need to install a dual system.2Install Ub

Tag: Code screen--line XOR does not have Mina content valueNvidia's graphics card is overclocking-enabled, with tools such as afterburning in Windows.But there is no such thing as a ready-made tool under Linux.But Coolbits's settings are also very simple.Just modify the xorg.conf file to add coolbit and you can overclock it with nvidia-setting.Manual editing is still a hassle, in fact Nvidia provides commands to implement this edit.$sudo nvidia-xconfig -a --cool-bits=24 --allow-empty-initial-con

When compiling the source code with VS compilation OpenCV, the CMake-generated engineering file compiles, and the NVCC fatal:unsupported GPU architecture ' compute_11 ' problem occurs. The reason is that CUDA7.5 does not support older graphics versions, so 1.1,2.0,2.1, such as graphics options, are redundant. Need to change the configuration of the CMake GUI for the project and remove support for Compute_11 1. Open Cmakelist.txt CMake in the option t

provided by the SDK can be used to test transfer performance from host to Device,device to Host,device to device. Although PCIe has a 3.2g/s theoretical value, it does not actually reach so much. The transmission of Device to Device can reach 89g/s (GTX260), and the theoretical value is 90g/s (GTX260) is about the same. This place is not the same for everyone, the motherboard is not the same, setting the environment is different, not necessarily the same. An active warp on device has 32 thread

Brief introduction This blog introduces kinectfusion in the ICP algorithm code, code implementation is the PCL Engineering Pcl_gpu_kinfu_large_scale project file ESTIMATE_COMBINED.CU. The ICP algorithm can greatly improve the computational efficiency by doing parallel computing with the GPU. The objective function in the GPU minimization ICP algorithm Kinectfusion in the ICP using the minimum point to th

Nowadays, AI is getting more and more attention, and this is largely attributed to the rapid development of deep learning. The successful cross-border between AI and different industries has a profound impact on traditional industries.Recently, I also began to keep in touch with deep learning, before I read a lot of articles, the history of deep learning and related theoretical knowledge also have a general understanding.But as the saying goes: The end of the paper is shallow, it is known that t

The method of referring to the great God: http://www.th7.cn/system/win/201603/155182.shtmlFirst step: Need to install CUDA, vs2013;cuda default path, note Cuda version and GPU to matchThe second step:. Download CUDNN, build a local folder under the Matconvnet folder, and put the CUDNN in (I changed the filename called CUDNN)Step three: Open vl_compilenn.m, Run, wait for compilation to finishThe fourth step is to copy the Cudnn64_4.dll under the bin to

D3d9 GPU HacksI ' ve been trying to catch up what hacks GPU vendors has exposed in Direct3D9, and turns out there's a lot of them!If you know more hacks or more details, please let me know in the comments!Most hacks is exposed as custom ("FOURCC") formats. So-check for the CheckDeviceFormat . Here's the list (Usage column codes:ds=depthstencil, Rt=rendertarget; Resource column codes:tex=texture, Surf=surfac

The most important Optimization of body rendering is to reduce GPU sampling. Testing the filling rate of the GPU material can guide our work. Do you want to know why the GPU can only reach 12 FPS in 800*600 environments? This depends on the number of GPU samples per second. I wrote a simple OSGProgramTo test the numb

of dll ). 2. next, the application delegates the NiD3DShader initialization work to NiShaderLibrary for processing. NiShaderLibrary first loads all shader text files through nid3dxjavastloader, and uses nid3dxjavastparser to parse the text to generate the nid3dxjavastfile object, at the same time, NiD3DXEffectLoader is responsible for compiling shader code into a binary form GPU program. 3. NiD3DXEffectTechnique is responsible for generating the NiD3

In order to practice English and share what I have learned about the instanced tessellation, I wrote this artical, just talking about the instance tessellation pipeline, not the mathematical research about the surface soomthing. -- zxx Days buried myself in *. CPP and *. PDF files, I finally got the idea of the instanced tessellation, which has been implemented in the earlier days after when dx10 is released and NVIDIA added a geometry process part to the G