Node.js Add-ons for High Performance Numeric Computing

" summary

Covers using Node.js native add-ons (C/C++) to run high-performance numeric computing and machine learning on the server. Explains the add-on toolchain, walks through compiling BLAS linear-algebra routines as Node add-ons, and shows how to extend the build process to incorporate Fortran libraries. Includes practical lessons, tips and tricks learned while writing these add-ons to maximize Node.js performance.

AI-written summary · about

" related talks

• 29mNode.js Addons for High Performance Numeric ComputingAthan Reines · NodeConf Argentina 2017
• 31mBuilding Modern Native Add-ons for Node.js in 2021Gabriel Schulhof, Kevin Eady · OpenJS World 2021
• 46m3D Graphics with Node.js AddonsLuis Blanko · HolyJS 2024 Autumn
• 25mIntroduction to native extensionsMariano Moretti · NodeConf Argentina 2018
• 17mJavaScript and mathematical computing across servers and clientsRudraksh MK · JSFoo 2014
• 34mWorker Threads vs C ++ Addons (part 2)Oleksii Novokhatskyi · VinnytsiaJS 2021
• 50mBuilding native Node.js addons in ReasonVladimir Kurchatkin · ReasonConf 2018
• 21mModern C++ Addons for Node.jsDvir Yitzchaki · ACCU 2024