cuda parallel programming

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

: Cuda Accelerated PDE (partial differential equation, partial differential equations) in the regular grid system LIBSVM MULTISVM in Open source database solution Cuda/gpu: Multi-level SVM with Cuda CUSVM: Cuda usage support for vector classification and attenuation 2. CUDA

" + "\ n") else:fout.write ("Positive" + "\ n") Fout.close ()Run the program to generate 4,000 dimensions of 8 data:The file "Input.txt" was generated:Second, serial code:This code is consistent with the previous article code, we select 400 data to be used as test data, 3,600 data for training data.knn_2.cc:#include Makefiletarget:g++ knn_2.cc./a.out 7 4000 8 INPUT.TXTCU:NVCC knn.cu./a.out 7 4000 8 Input.txtOperation Result:Third, parallel implementat

The previous introduction of basic CUDA programming knowledge, then this article to see the GPU in the processing of data calculation of the efficiency, we take the matrix multiplication as an example. performs matrix multiplication and performance on 1.CPU. Code for matrix multiplication on the CPU: mat_mul.cc: A[i]*b[i] + c[i] = D[i] #include wtime.h: #ifndef _wtime_ #define _WTIME_ double wtime

kernel function inside can understand.line68:"1" in Compute_sum is the number of blocks, "count" is the number of threads inside each block, "blockshareddatasize" is the size of the shared memory.Kernel function Compute_sum:line35: defines the shared memory variable.Line36: The memory area of the corresponding sharedmem of threadidx.x smaller than CNT is assigned to the value in array array.line39~47: The function of this code is to add all the values and place them in the sharemem[0] position.

Prior to the introduction of basic CUDA programming knowledge, then this article on the basis of the GPU in processing data calculation of the efficient performance, we take the matrix multiplied as an example.Performs matrix multiplication and performance on 1.CPU.The code for the Matrix multiplication operation on the CPU:mat_mul.cc:wtime.h:wtime.cc:MakefileResults:Performs matrix multiplication and perfo

In the process of image processing, we often use the gradient iteration to solve large-scale present equations; today, when the singular matrix is solved, there is a lack of DLL;Errors such as:Missing Cusparse32_60.dllMissing Cublas32_60.dllSolution:(1) Copy the Cusparse32_60.dll and Cublas32_60.dll directly to the C:\Windows directory, but the same error will occur at all times, in order to avoid trouble, it is best to use the method (2)(2) Copy Cusparse32_60.dll and Cublas32_60.dll to the file

the skeleton of a CUDA program has been set up, and the top priority of GPU computing is the parallel acceleration has not been introduced, but before the acceleration we have a very important thing to consider, that is whether our program is accelerated, that is, we want to output the program run time, This time we need to use CUDA provides a clock function, yo

designed to take into account the program execution and data operations parallelism, versatility and their balance. The micro-architecture of GPU is designed for the numerical calculation of matrix type, which is a large number of iterative design of computational units, which can be divided into many independent numerical calculations-a large number of numerical operations of the thread, and the data is not like the logic of the implementation of the logical correlation.However, after all, Mic

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

I. Concept. 1. Related keywords. CUDA (Compute Unified Device Architecture). GPU English full name graphic processing unit, Chinese translation as "graphics processor." 2. Cuda is a general-purpose parallel computing architecture introduced by NVIDIA, which enables the GPU to solve complex computational problems. It contains the

When Cuda C is run in the cudart library, the application can be linked to the static library cudart. lib or libcudart. A. The dynamic library cudart. dll or libcudart. So. The Cuda dynamic link library (cudart. dll or libcudart. So) must be included in the installation package of the application. All running functions of Cuda are prefixed with

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

; 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

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 core

CUDA and cuda ProgrammingIntroduction to CUDA Libraries It is the location of the CUDA library. This article briefly introduces cuSPARSE, cuBLAS, cuFFT and cuRAND will introduce OpenACC later. The cuSPARSE linear algebra library is mainly used for sparse matrices. CuBLAS is a C