In this post I will be compiling NAMD from source for good performance on modern GPU accelerated Workstation hardware. Doing a custom NAMD build from source code gives a moderate but significant boost in performance. This can be important considering that large simulations over many time-steps can run for days or weeks. I wanted to do some custom NAMD builds to ensure that that modern Workstation hardware was being well utilized. I include some results for the STMV benchmark showing the custom build performance boost. I’ve included some results using NVIDIA 1080Ti and Titan V GPU’s as well as an “experimental” build using an Ubuntu 18.04 base.
If you are wanting to use Ubuntu 18.04 and also want a CUDA install this post should help you get that working.
In this post I’ll be going over details of Installing Ubuntu 18.04 including the NVIDIA display driver and, any one of the available desktop environments. I’ll do this starting from a base server install. I’ll go over a few possible pitfalls and end with a short discussion on the new netplan configuration tool for Ubuntu networking.
TensorFlow is a very important Machine/Deep Learning framework and Ubuntu Linux is a great workstation platform for this type of work. If you are wanting to setup a workstation using Ubuntu 18.04 with CUDA GPU acceleration support for TensorFlow then this guide will hopefully help you get your machine learning environment up and running without a lot of trouble. And, you don’t have to do a CUDA install!