people who understand the JPEG data format should be able to imagine that the method of splitting and compressing images with 8*8 pixel block size is very easy to implement with parallel processing ideas. In fact, Nvidia's Cuda has provided examples of JPEG codecs since v5.5. The example is stored in the Cuda SDK, the default installation path for Cuda "C:\Progra
Caffe is a very clear and efficient deep learning framework, now has a lot of users, but also gradually formed their own community, the community can discuss related issues.
I began to look at the relevant content of deep learning to be able to use Caffe training to test their own data, see a lot of sites, tutorials and blogs, also took a lot of detours, the whole process to comb and summarize, in order to expect can be easily through this article can be easy to use Caffe training their data, Ex
In recent days want to c,cuda,mpi mixed compiled Linux to rewrite the dynamic link library libtest.so, after two or three days of the first large variety of search information, turn over a variety of makefile files, all kinds of reading blog, finally. Finally, I'm crying for joy.
1. First understand how the CPU side to encapsulate code into a dynamic link library
Reprint Address: http://www.cnblogs.com/huangxinzhen/p/4047051.html
Of course, a lot of r
CUDA, cudagpuMemory
The level of kernel performance cannot be simply explained from the execution of warp. As mentioned in the previous blog post, setting the block dimension to half the warp Size will reduce the load efficiency, which cannot be explained by the scheduling or parallelism of warp. The root cause is the poor way to get global memory.
As we all know, memory operations play a very important role in efficiency-oriented languages. Low-laten
I just read something about Cuda and planned to write a program. As a result, I encountered a bunch of problems. The first problem is the array transfer problem on the host and device, which is a bit dizzy. After reading some information, I will summarize it as follows.
1: How did the problem come about?
One-dimensional array, two-dimensional array, and three-dimensional array are used on device. For one-dimensional arrays, cudamalloc and cudamemcpy a
This section describes the main concepts of the Cuda programming model.
2.1.kernels (kernel function)
Cuda C extends the C language and allows programmers to define C functions, called kernels ). Execute n times in N Cuda threads in parallel.
Use the _ global _ specifier to declare a core function, call and use
For example, add two vectors, add a and B, and stor
Http://blog.csdn.net/yutianzuijin/article/details/8147912category: Programming Language 2521 people read comments (0) Add to favorites report cudagpu
Recently, I first tried Cuda programming. As a newbie, I encountered various problems and spent a lot of time solving these incredible problems. In order to avoid people from repeating the same mistakes, we will summarize the problems we have encountered as follows.
(1). cudamalloc
The first time I used
Cuda Basic Concept Cuda grid limits 1.2CPU and GPU design differences 2.1cuda-thread2.2cuda-memory (storage) and Bank-conflict2.3cuda matrix multiplication 3.1 Global storage bandwidth and consolidated access Memory (DRAM) bandwidth and memory coalesce3.2 convolution 3.3 analysis of the multiplexed 4.1Reduction model of convolution multiplication optimization 4.2 CUDA
The environment configured in this article is redhat6.9 + cuda10.0 + cudnn7.3.1 + anaonda6.7 + theano1.0.0 + keras2.2.0 + jupyter remote, with Cuda version 10.0. Step 1: before installing Cuda: 1. Verify if GPU is installed $ Lspci | grep-I NVIDIA 2. Check the RedHat version. $ Uname-M CAT/etc/* release 3. After the test is completed, download Cuda from the
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
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
1. The Block and threading concepts in Cuda can be expressed in the following diagram:Each grid contains a block (block) that can be represented by a two-dimensional array, and each block contains a thread that can be represented by a two-dimensional array.2. Two-d array blocks and threads can be defined with DIM3:DIM3 Blockpergrid (3,2); Defines a 3*2=6 blocksDIM3 Threadsperblock (3,3);//define 3*3=9 threads3. How does the code for each thread in the
Sometimes due to cuda upgrade or download source of the original creation of the project is different from the Cuda version, when the project was opened found not loaded, prompted: Imported items not found "C:\Program Files (x86) \msbuild\microsoft.cpp\ V4.0\buildcustomizations\cuda 5.0.props "Workaround:Locate the. vcxproj file in your project, open it with Note
I have recently learned how to use Cuda to accelerate image processing.
The following describes a project example in codeproject. Image filtering is performed using Cuda.
Web: http://www.codeproject.com/Articles/206036/Image-Filters-using-CPU-and-GPU
The process is as follows:
You can also read and process data from a video file.
The main class diagram is as follows:
Isingleimagefilter is an abstr
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
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
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
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