, start to think about the relationship between GPU and particles. Conclusion: When the CPU initializes the particle system, there can be surplus data and data can be duplicated, but it must comply with the GPU Data Processing Method: there is no data dependency between each particle; each vertex in the particle has no data dependency. The complete life process of a particle only depends on the initial data
1. The GPU and OCL modules are not enabled for the libraries provided in the Development Kit provided by opencv. Although there are *** GPU. lib/*** GPU. DLL files, they cannot be used. If you call GPU: getcudaenabledevicecount (), return 0. To enable this function, you need to re-compile the library of opencv.
2. Ref
Install theano and configure GPU in Win10, win10theano
I. Software and Environment
(1) installation date;
(2) Raw Materials VS2013, cuda-8.0 (it is best to download cuda7.5, the current theano-0.8.2 for cuda-8 support is not very good), Anaconda3-4.2.0 (64-bit );
(3) The environment is win10.
Ii. Installation Steps
(1) install VS2013. There is nothing to say. After downloading the 64-bit version, you just need to take the next step. Remember to insta
Recently started a GTX 1070 notebook, preface want to Win10 on the GPU run model, so there is the next installation GPU version of the bumpy course of mxnet, after multiple experiments finally fixed python and R installation Mxnet, the main points are recorded as follows:I mainly refer to these 2 blog posts:https://my.oschina.net/qinhui99/blog/845249http://blog.csdn.net/u010414386/article/details/533041771.
Installing Theano
Configuring the GPU
"Original" Liu_longpoReprint Please specify the source "CSDN" http://blog.csdn.net/llp1992Installing TheanoThis post is an experience and I hope to help those who have struggled with me.Already said, non-root users, so can not use sudo, only this series of trouble.To install Theano, you need some dependencies, and you can refer to the blog for details:Deeplearning (i) Best en
Respect for the wisdom of others, welcome reprint, please indicate the author's heart if Transparent address http://www.cnblogs.com/ubanck/p/4110618.htmlIn the previous blog, roughly explained the principle of 3D rendering, that is, from a simple model to the process of rendering to the screen! It mentions the important coordinate transformation way, said not clear! Today to talk about the implementation of the shader languageHardware, the GPU has a v
Tags: blog from the This COM update inter for pass ALS1. Show current GPU usageNvidia-smi2. Usage of the periodic input GPUUse the watch command to periodically output GPU usage$ Whatis WatchWatch (1)-Execute a program periodically, showing output fullscreen$watchUsage:Watch [Options] CommandOptions:-B,--beep beep if command has a Non-zero exit-C,--color interpret ANSI color sequences-D,--differences[=Highl
Statement
This document is only for learning and exchange, please do not use for other commercial purposesAuthor: Chaoyang _tonyE-mail:linzhaolover@163.comCreate date:2018 Year April 8 20:29:38Last change:2018 year April 8 20:29:50Reprint please indicate the source: Http://blog.csdn.net/linzhaolover Summary
A recent need to build an environment requires the physical machine's GPU card to be mapped to the KVM for use. That is, passthrough on the Inter
1. Display current GPU usage
Nvidia has a Nvidia-smi command-line tool that displays video memory usage:
$ nvidia-smi1 1
Output:2. Periodic output GPU usage
But sometimes we want to not only know the GPU usage at that fixed moment, we want to keep it going, we want to output periodically, like updating the display every 10s. At this point, you need to use the Wa
Change at a glance:
Last month, the 9-year long stay in the beta version of the graphics card first identification tool Gpu-z released the first official version of v1.9.0, during a total of 89 versions of the evolution.
Today, the v1.10.0 release adds support for new cards such as AMD RX470460, Nvidia TITAN X, and so on, in the near period.
Functional changes:
-Increase support for Radeon Rx 470, RX 460
-Increased support for Nvidia GTX TITAN X
It is best to apply thermal grease between the heatsink and the CPU when installing the cooling fan. The role of silicone grease is not only the heat produced by the CPU quickly and evenly passed to the heat sink, in many cases, silicone grease can also increase the heat sink not too flat under the surface of the heat contact with the CPU.
and silicone grease has a certain viscosity, in the fixed heat sink metal shrapnel slightly aging loose, you can to a certain extent so that the heatsink wil
. As long as there is this concept, the purpose of my article is achieved. Front of the "Cuda Hardware Implementation Analysis (i)------Camp-----GPU Revolution" has explained the thread in the cuda of the concrete running process. Let's look at some of the provisions in the CUDA hardware implementation. This is more reasonable, army camp, it should be promulgated rules system, only understand the rules of CUDA system, can really put the various thread
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
I feel that the amp code is very understandable.
I. VC ++ 11 code
1: #include "stdafx.h"
2: #include
3:
4: using namespace concurrency;
5:
6: extern "C" __declspec ( dllexport ) void _stdcall square_array(float* arr, int n)
7: {
8: // Create a view over the data on the CPU
9: array_view
10:
11: // Run code on the GPU
12: parallel_for_each(dataView.extent, [=] (index
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