Shulou(Shulou.com)06/02 Report--

In this issue, the editor will bring you about how to use Python to provide high-performance computing services. The article is rich and analyzed and described from a professional point of view. I hope you can get something after reading this article.

Preface

Python has a rich library and can easily be used as a glue language for interactive integration with cUniverse +.

Therefore, in order to adapt to the rapidly changing business and take into account computing efficiency, it is a very suitable way for algorithm developers to use python as server to provide service in the upper layer and cAccord + in the bottom layer.

The python flask library provides http interfaces and related demo pages, gunicorn provides multi-core parallel capabilities, and the underlying C++ library provides single-threaded computing.

Here is an example to illustrate this architecture. Code address: python_hps

Prepare for

Flask, gunicorn, apach bench tool and other tools need to be installed before the lab begins.

Note: all experiments are carried out in the linux system. The test machine is a 4-core virtual machine.

Sudo pip install flask sudo pip install gunicorn sudo apt-get install apache2-utils

Calculate

The calculation part simulates the real computing, so the amount of calculation is relatively large. On the virtual machine I tested, the single-core and single-thread runs around 400ms.

C++ core computing part, casually written:

API_DESC int foo (const int val) {float result = 0.0f; for (int caterpillar)

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