Detailed introduction to cuda

The following editor will bring you an article on how to use Python to write CUDA programs. The editor thinks it’s pretty good, so I’ll share it with you now and give it as a reference. Let's follow the editor and take a look. There are two ways to write CUDA programs in Python: * Numba * PyCUDAnumbapro is no longer recommended. The functions have been split and integrated into accelerate and Numba respectively. Example numbaNumba optimizes Python code through the just-in-time compilation mechanism (JIT). Numba can be optimized for the local hardware environment, supports both CPU and GPU optimization, and can be integrated with Numpy so that Python code can run on the GPU, just by Add the relevant instruction mark above the function, as shown below: import numpy as np from timeit import default_timer as timer from 

1. Detailed introduction to how to use Python to write CUDA programs

##Introduction: The editor below will bring you an article on how to use Python to write CUDA programs. The editor thinks it’s pretty good, so I’ll share it with you now and give it as a reference. Let’s follow the editor and take a look

2. An application of MySQL compressed table

Introduction: 1 .Set the server parameter innodb_file_per_table=ONinnodb_file_format=Barracuda 2. Create a table or modify the table parameter alter table sod_song_log_2014

3. ubuntu14.04+cuda6.5+opencv2.4.9+cuda

Introduction: This is the first time I write a technical blog in such a formal way. Firstly, I want to practice my ability to write summaries. Secondly, it is because I read a lot of technical posts but never contribute. I feel ashamed of myself. 1. Preparation 1. First of all, the running environment is ubuntu14.04, so this article assumes that everyone has installed ubuntu14.04. In addition, cuda is the programming architecture designed by nvidia for its own GPU, so

4. OpenMP and MPICH2 data distribution and sharing during cluster computing

Introduction: For cluster computing, Using MPICH2 to connect and control each node, and using OpenMP to fully parallelize the CPU and each CPU core within the node is a relatively low-cost and foolproof solution. (Heterogeneous computing is expected to require the participation of OpenCL or CUDA, but I have never done it). MPI (CH2) is a parallelization technology applied to distributed computing facilities, and OpenMP corresponds to it

The above is the detailed content of Detailed introduction to cuda. For more information, please follow other related articles on the PHP Chinese website!