cuda in python

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

editing, Python implementation, the original is mainly deployed in Ubuntu, but also the great God released the Windows version, but other relevant information is less, not suitable for novice use, so or Ubuntu is more suitable for beginners. RelativelyThis article contains 5 parts, including: The first part of Linux installation Part II installation of NVidia CUDA Toolkit (*.deb method) Pa

Two-dimensional FFT in cuda-cufftExecC2C, cuda-cufftexecc2c #include

is compatible but considered legacyBLAS via ATLAS, MKL, or Openblas.Boost >= 1.55OpenCV >= 2.4 including 3.0Protobuf, Glog, gflagsIO libraries Hdf5, LEVELDB, snappy, LmdbPycaffe and Matcaffe interfaces have their own natural needs.For Python caffe:python 2.7 or Python 3.3+, numpy (>= 1.7), boost-provided Boost.pythonFor MATLAB Caffe:matlab with the Mex compiler.CuDNN caffe:for fastest Operation Caffe is ac

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 intended to allow the code to run concurrently o

It's a hard job to install cuda and optumus on Kali Linux, I tried all day and finally success, this is how it words. Install cuda and nvidia driverIt's really simple, and it may take some time, it's not the latest version, but it works. Apt-get updateApt-get install nvidia-detect nvidia-libopencl1 nvidia-opencl-common nvidia-support nvidia-opencl-icd nvidia-visual-profiler nvidia-glx nvidia-installer-clean

-linux-x64-v5.1 Find the download path, CD in, find this file, enter the following actions: Tar xvzf cudnn-8.0-linux-x64-v5.1-prod.tgz sudo cp cuda/include/cudnn.h/usr/local/cuda/include sudo cp cuda/lib64/lib cudnn*/usr/local/cuda/lib64 Complete the above steps, CUDNN installation is complete. 9. Next, install the T

CUDA 3, CUDAPreface The thread organization form is crucial to the program performance. This blog post mainly introduces the thread organization form in the following situations: 2D grid 2D block Thread Index Generally, a matrix is linearly stored in global memory and linear with rows: In kernel, the unique index of a thread is very useful. To determine the index of a thread, we take 2D as an example: Thread and block Indexes Element coordinates

=2; - float*x_h, *x_d, *y_h, *Y_d; -X_h = (float*) malloc (n *sizeof(float)); -Y_h = (float*) malloc (n *sizeof(float)); + for(inti =0; I ) - { +X_h[i] = (float) I; AY_h[i] =1.0; at } -Cudamalloc (x_d, n *sizeof(float)); -Cudamalloc (y_d, n *sizeof(float)); -cudamemcpy (X_d, X_h, n *sizeof(float), cudamemcpyhosttodevice); -cudamemcpy (Y_d, Y_h, n *sizeof(float), cudamemcpyhosttodevice); -Saxpy 1, ->>>(A, x_d, Y_d, n); incudamemcpy (Y_h, Y_d, n *sizeof(float), cudamemcpydeviceto

OpenCV read the picture and pass the picture data to Cuda processing #include Reference code: Calculate PI #include

explanation of the relationship between Cuda and opencl. They are not conflicting, but inclusive. Opencl is an API. At the first level, the Cuda architecture is at a higher level. In this architecture, whether it is an API like opencl or dx11, or computing like C, Fortran, or dx11, are supported. As program developers, they generally only understand these languages or APIs and can develop their own program

Caffe (convolution Architecture for Feature Extraction) as a very hot framework for deep learning CNN, for Beginners, Build Linux under the Caffe platform is a key step in learning deep learning, its process is more cumbersome, recalled the original toss of those days, then summed up the Ubuntu14.04 configuration process, convenient later novice can be less detours. 1. Installing Build-essentials Install some basic packages needed for development sudo apt-get install build-essential2. Installin

Ubuntu Software Center and then put all the displayed items on the installation.3. Install additional dependencies: $ sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-s Erial-dev Libgflags-dev Libgoogle-glog-dev Liblmdb-dev protobuf-compiler (one installation recommended)4. Download Caffe, go to the Python directory under the Caffe directory: $ sudo apt-get install

) Batch_size]},allow_input_downcast= True) Lin155, Test_model_all = Theano.function ([x, y], Test_error,allow_input_downcast=true)(3) running the programTheano_flags=mode=fast_run,device=gpu0,floatx=float32,warn_float64=raise python conv_net_sentence.py-static- Word2vecTheano_flags=mode=fast_run,device=gpu0,floatx=float32,warn_float64=raise python conv_net_sentence.py-nonstatic- Word2vecTheano_flags=mode=fa

I. Basic CONCEPTS1. CUDAIn 2007, NVIDIA launched the programming model of CUDA (Compute Unified device Architecture, unified Computing Device architecture) in order to make full use of the advantages of CPUs and GPUs in the application for CPU/GPU joint execution. The need for this co-execution has been reflected in the latest centralized programming model (opencl,openacc,c++ AMP).2. Parallel programming languages and modelsThe most widely used are th

~/ Nvidia_cuda-7.5_samples/bin/x86_64/linux/release ./devicequery CD ~/nvidia_cuda-7.5_samples/1_utilities /bandwidthtest make ./bandwidthtest1 2 3 4 5 6 7 8 9 10 11 If two test results are pass, it means that Cuda is running normally. Reference links Cuda-7.5-toolkit 2. Install TensorFlow Essential Python-pip and Python