Talk for NodeConf Argentina (2017).
Node.js add-ons allow native code to be run from the Node.js runtime. In this talk, I will discuss how to utilize native add-ons for high performance numeric computing and machine learning in Node.js applications. I will first provide an overview of add-ons and their associated toolchain. Next, I will walk through an example which involves compiling basic linear algebra subprograms (BLAS), a suite of libraries which are a core foundation of most modern numeric computing environments, as native add-ons. While Node.js add-ons are oriented toward C and C++, I will show how to extend compilation support to Fortran libraries in order to maximize computational performance. Throughout the talk, I will offer lessons learned and other insights gained while writing add-ons and demonstrate why Node.js is an excellent environment for high performance numeric computing and machine learning.
$ git clone https://github.com/kgryte/talks-nodeconf-argentina-2017.gitand
$ npm installFrom the top-level directory,
$ python -m SimpleHTTPServer 9000and, in your browser, navigate to
http://127.0.0.1:9000
Copyright © 2017. Athan Reines.