cuda programming python

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

) start = Timer () F UNC (DRV. InOut (a), DRV. In (b), N, block= (ntheads, 1, 1), grid= (Nblocks, 1)) Run_time = Timer ()-Start print ("GPU Run time%f seconds "% run_time) # CPU Run start = timer () AA = (AA * 10 + 2) * ((b + 2) * 10-5) * 5 run_time = timer ()-Start print ("CPU run time%f seconds"% run_time) # Check Res Ult r = A-aa print (min (r), Max (R)) def Main (): For n in range (1, ten): n = 1024x768 * 1024x768 * (n *) print ("----------- -%d---------------"% n) test (n) If name = = ' Ma

Cuda Programming Model The Cuda programming model uses the CPU as the host, and the GPU as the co-processor or device. In this model, the CPU is responsible for logic-Oriented Transaction Processing and serial computing, while the GPU focuses on highly threaded parallel processing tasks. The CPU and GPU each ha

: 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

Software Version Window10 X64 Python 3.6.4 (64-bit) CUDA CUDA Toolkit 9.0 (Sept 2017) CuDNN CuDNN v7.0.5 (Dec 5), for CUDA 9.0 The above version of the test passed.Installation steps:1. to install

vs2015+cuda8.0 Environment Configuration Anyway, record the correct configuration here: 1, first, the officer network download corresponding vs version of Cuda Toolkit: Https://developer.nvidia.com/cuda-toolkit-50-archive (Remember vs2010 corresponds to cuda5.0,vs2013 corresponds to cuda7.5,vs2015 corresponding to CUDA8.0) 2, then, the direct installation, remember in the installation process if you do not

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 sum

Setting up CUDA programming in Ubuntu is actually very simple. Only one thing to note is the driver. I don't know why NVIDIA also provides the cudadriver_2.3_linux_32_190.18 driver when downloading CUDA, I tried it. Although the driver can be installed normally, an error will pop up when the graphic interface is started, and the graphic interface cannot be starte

Link addr One: Run the programAccording to the previous article, after installing the Cuda software, you can use the "nvcc-v" command to view the compiler version used, I use the version information from: "Cuda compilation tools, Release 3.2, V0.2.1221." Create a directory yourself, in which the new CU file, write code, save, you can use the terminal to switch to the corresponding directory to compile, comp

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.

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. ./

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

" + "\ 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 implementationParallel implementation of the process is

This program is to add two vectorsAddTid=blockidx.x;//blockidx is a built-in variable, blockidx.x represents this is a 2-D indexCode:/*============================================================================Name:vectorsum-cuda.cuAuthor:canVersion:Copyright:your Copyright NoticeDescription:cuda Compute reciprocals============================================================================*/#include using namespace Std;#define N 10__global__ void Add (int *a,int *b,int *c);static void Checkcud

char) (128.0f+127.0f*cos (d/10.0f-ticks/7.0f)/(d/10.0f+1.0f));Ptr[offset*4+0]=grey;Ptr[offset*4+1]=grey;Ptr[offset*4+2]=grey;ptr[offset*4+3]=255;}int main (){DataBlock data;Cpuanimbitmap bitmap (Dim,dim,data);Data.bitmap = bitmap;Cuda_check_return (Cudamalloc (void * *) data.dev_bitmap,bitmap.image_size ()));Bitmap.anim_and_exit ((Void (*) (Void*,int)) Generate_frame, (Void (*) (void*)) cleanup);}void Generate_frame (DataBlock *d,int ticks){A total of dimxdim pixels, each pixel corresponding to

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

Cuda Programming Interface (ii) ------ 18 weapons ------ GPU revolution 4. Program Running Control: operations such as stream, event, context, module, and execution control are classified into operation management. Here, the score is clearly at the runtime level and driver level. Stream: If you are familiar with the graphics card in the Age of AGP, you will know that when data is exchanged between the de

::operator *") is not allowedcalling a host function("cuComplex::cuComplex") from a __device__/__global__ function("cuComplex::operator +") is not allowed This is because there is a problem with the Code provided in the original work. The code in the structure in the original work is cuComplex(float a, float b) : r(a), i(b) {} Modify it as follows: __device__ cuComplex(float a, float b) : r(a), i(b) {} Question 2 Error lnk2019: an external symbol that cannot be parsed [email protected]. This

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

I want to learning deep learning, so config Cuda is a essential step. Luckily it's very easy in UbuntuInstall Theano+cuda in Ubuntu1. Install TheanoA) sudo apt-get install python-numpy python-scipy python-dev python-pip