cuda programming book

; unsigned int col_idx = threadIdx.x * blockDim.y + threadIdx.y; // shared memory store operation tile[row_idx] = row_idx; // wait for all threads to complete __syncthreads(); // shared memory load operation out[row_idx] = tile[col_idx];} Shared Memory: SetRowReadColDyn View transaction: Kernel: setRowReadColDyn(int*)1 shared_load_transactions_per_request 16.0000001 shared_store_transactions_per_request 1.000000 The result is the same as the previous example, bu

One, using the GPU module provided in the OPENCV At present, many GPU functions have been provided in OpenCV, and the GPU modules provided by OPENCV can be used to accelerate most image processing. Basic use method, please refer to: http://www.cnblogs.com/dwdxdy/p/3244508.html The advantage of this method is simple, using Gpumat to manage the data transfer between CPU and GPU, and does not need to pay attention to the setting of kernel function call parameter, only need to pay attention to the l

Document directory Function qualifier Variable type qualifier Execute Configuration Built-in Variables Time Functions Synchronous Functions 1. Parallel Computing 1) Single-core command-level parallel ILP-enables the execution unit of a single processor to execute multiple commands simultaneously 2) multi-core parallel TLP-integrate multiple processor cores on one chip to achieve line-level parallel 3) multi-processor parallelism-Install multiple processors on a single circuit board and i

Install cuda6.5 + vs2012, the operating system is win8.1 version, first of all the next GPU-Z detected a bit: It can be seen that this video card is a low-end configuration, the key is to look at two: Shaders = 384, also known as Sm, or the number of core/stream processors. The larger the number, the more parallel threads are executed, and the larger the computing workload per unit time. Buswidth = 64bit. The larger the value, the faster the data processing speed. Next let's take a look at the

In this paper, the basic concepts of CUDA parallel programming are illustrated by the vector summation operation. The so-called vector summation is the addition of the corresponding element 22 in the two array data, and the result is saved in the third array. As shown in the following:1. CPU-based vector summation:The code is simple:#include the use of the while loop above is somewhat complex, but it is int

series solved with this method)Log in with super privileges, set environment variablesCommand: sudo gedit/etc/profileEnter at the bottom of the document: (Hint: The path entered after Pythonpath= is the Caffe path installed under Linux)Pythonpath=caffe/python: $PYTHONPATHExport PYTHONPATHCommand: Source/etc/profilePythonImport Caffe6.test:Command: Python draw_net.py e.g. ./python/draw_net.py./examples/mnist/lenet_train_test.prototxt lenet.pngNote: The Graphviz and Pydot are installed firstComma

Learning computer image processing algorithm of children's shoes, you have to learn Cuda, why. Because image processing is usually a matrix operation, it is very important to calculate the calculation time of millions at this time is essential. OPENCV itself provides a number of CUDA functions that meet the needs of most users. But not absolutely, sometimes we need to define a kernel function to optimize, o

Book DescriptionCuda is a computing architecture designed to facilitate the development of parallel programs. in conjunction with a comprehensive software platform, the Cuda architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demaning graphics and game applications.

A while ago, I completed both the ant colony algorithm and the improved K-Means algorithm, and then watched CUDA programming. I read the introduction of CUDA and thought that CUDA would be easy to use after C, in fact, you still need to know some GPU architecture-related knowledge to write a good program. After reading

A while ago, I completed both the ant colony algorithm and the improved K-means algorithm, and then watched Cuda programming. I read the introduction of Cuda and thought that Cuda would be easy to use after C, in fact, you still need to know some GPU architecture-related knowledge to write a good program. After reading

Cuda C provides a simple way for people familiar with the C programming language to write code executed on a device (GPU. It consists of a minimal C Language extension set and Runtime Library. Core language extensions have been introduced in the programming model section. Allow programmers to define core functions and use New syntaxes to specify the grid and bloc

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 ex

Bo Master due to the needs of the work, began to learn the GPU above the programming, mainly related to the GPU based on the depth of knowledge, in view of the previous did not contact GPU programming, so here specifically to learn the GPU above programming. Have like-minded small partners, welcome to exchange and study, my email: caijinping220@gmail.com. Using t

Abstract: This article describes the basic methods for compiling windows console application, dynamic link library (DLL), and Cuda c dll in. net. 1. Write windows console application in Cuda C Next we will learn Cuda C from a simple example. Open Vs and create a cudawinapp project. The project name is vector and the solution name is cudademo. Click "OK", "Next",

Transferred from: http://m.blog.csdn.net/blog/oHanTanYanYing/39855829This article is about how CPP files call Cuda. cu files for graphics acceleration related programming. Of course, this is done in the case where Cuda is already configured by default, and if you have questions about how to configure Cuda, you can read

in the following way:Cudaeventdestroy (start); Cudaeventdestroy (stop);② the past timeThe events created by the section can be timed to the code of the 3.2.5.5.1 section in the following way:Cudaeventrecord (Start,0); For(intI=0; I2;++i) {Cudamemcpyasync (Inputdev+I*Size, Inputhost+I*Size, size, Cudamemcpyhosttodevice, stream[i]); MYKERNEL512,0, Stream[i]>>>(Outputdev+I*size, Inputdev+i* size, size); Cudamemcpyasync (Outputhost+ i*size, outputDevi *size, size, Cudamemcpydevicetohost, Stream[i])