cuda sdk

Wasting time = wasting life, Cuda prolonging your life Once, the author of a web design work in the art friend told me a joke, their units for a large-scale promotional activities, ready to design a giant banner ads, and banner ads Design task is naturally their art responsibility. But it was not my friend who was specifically responsible for the design, but a few of his colleagues. According to my friend's account, two colleagues, also designed for

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

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

Caffe is an efficient, deep learning framework. It can be executed either on the CPU or on the GPU.The following is an introduction to the Caffe configuration compilation process on Ubuntu without Cuda:1. Install the blas:$ sudo apt-get install Libatlas-base-dev2. Install dependencies: $ sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-devlibopencv-dev Libboost-all-dev libhdf5-se Rial-dev Protobuf-compiler Liblmdb-dev3. Install Glog (down

Preface: Today may be a relatively bad day, from the first phone in the morning, to the afternoon some of the things, some Xu lost. Sometimes really want to work and life completely separate, but who can really split so open, Mashikamu! A lot of times want to give life some definition, add some comments. But life is inherently a code that doesn't need to be annotated. Explain with 0来? Or is it an explanation? 0, the beginning of Heaven and earth, 1, the source of all things. Who can say clearly,

Let's take a look at the GPU test sample for the OPENCV background modeling algorithm: #include OPENCV provides some basic processing of CUDA programming, such as copying images from CPU to GPU (Mat-to-Gpumat), Upload,download. OPENCV encapsulation and shielding the cuda underlying functions, this has the advantage and disadvantage, for some people interested in algorithmic applications, very good, as lo

Vc6.0 latest SDK Platform SDK xp-sp2 official 20:19:09 2010-09-03 It can be seen that Microsoft no longer supports vc6, and the latest SDK cannot be used on vc6. However, you can find the last two versions that support vc6:For server2003 3790.0 RTM: size (bytes): 342,000,000 last updated: February 2003For xpsp2 2600.2180 RTM: size (bytes): 266,000,000 la

In the fifth lecture, we studied the GPU three important basic parallel algorithms: Reduce, Scan and histogram, and analyzed its function and serial parallel implementation method. In the sixth lecture, this paper takes the Bubble sort, merge sort, and sort in the sorting network, and Bitonic sort as an example, explains how to convert the serial parallel sorting method from the data structure class to the parallel sort, and attach the GPU implementation code.In the parallel method, we will cons

Original article link Today, we will introduce the relevant properties of Cuda devices. We can write code that is more suitable for hardware work only when we are familiar with the hardware and how it works. The cudadeviceprop struct records the properties of the device. 1 struct cudadeviceprop 2 {3 char name [256];/** Use cudagetdeviceproperties () to obtain the device attribute. Use cudagetdevicecount () to obtain the number of devices. Use cudacho

Source code and running result Cuda: https://github.com/zhxfl/cuCNN-I C language version reference from: http://eric-yuan.me/ The accuracy of the mnist library for famous handwritten numbers recognition is 99.7%. In a few minutes, CNN training can reach 99.60% accuracy. Parameter configuration The network configuration uses config.txt for configuration # comments between them, and the code will be filtered out automatically. For other formats, refer

Syntax highlighting in addition to the look comfortable, you can use F11 to find functions, variable definitions, hitting the function will also have a corresponding hint.The following is a set of code highlighting.In the Helloworldcuda.cu file above, the Cuda C + + keyword __global__ and so on are not highlighted, and there is a stroke curve. The following syntax highlighting of Cuda C + + keywords and fun

1. when compile /home/wangxiao/NVIDIA-CUDA-7.5 SAMPLES, it warning: gcc version larger than 4.9 not supported, so:old verson of gcc and g++ are needed: sudo apt-get install gcc-4.7 sudo apt-get install g++-4.7 Then, a link needed:sudo ln-S/Usr/Bin/gcc-4.7 / usr/local/cuda/bin/gccsudo ln - s /usr/bin /g++-4.7/usr/local/ cuda/bin/g ++ When c

What is CUDA Toolkit?For developers using C and C + + to develop GPU- accelerated applications, NVIDIA CUDA Toolkit provides a comprehensive development environment. CUDA Toolkit includes a compiler for Nvidia GPUs, many math libraries, and a variety of tools that you can use to debug and optimize application performance. You'll also find programming guides, use