The much anticipated NVIDIA GeForce RTX3080 has been released. How good is it with TensorFlow for machine learning? How about molecular dynamics with NAMD? I’ve got some preliminary numbers for you!
Does Enabling WSL2 Affect Performance of Windows 10 Applications
WSL2 offers improved performance over version 1 by providing more direct access to the host hardware drivers. Recent “Insider Dev Channel” builds of Win10 even allows access to the Windows NVIDIA display driver for GPU computing applications for WSL2 Linux applications! The performance improvements with WSL2 are largely because this version is running as a privileged virtual machine on to of MS Hyper-V. This means that at least low level support for the Hyper-V virtualization layer needs to be enabled to use it. In particular, the Windows feature “VirtualMachinePlatform” must be enabled for WSL2. We tested to see if there was any negative application performance impact.
Note: How To Install JupyterLab Extensions (Globally for a JupyterHub Server)
The current JupyterHub version 2.5.1 does not allow user installed extension for JupyterLab when it is being served from JupyterHub. This should be remedied in version 3. However, even when this is “fixed” it is still useful to be able to install extensions globally for all users on a multi-user system. This note will show you how.
Note: How To Copy and Rename a Microsoft WSL Linux Distribution
WSL on Windows 10 does not (currently) provide a direct way to copy a Linux distribution that was installed from the “Microsoft Store”. The following guide will show you a way to make a working copy of an installed distribution with a new name.
Note: Self-Signed SSL Certificate for (local) JupyterHub
In this note I’ll go through creating self-signed SSL certificates and adding them to a JupyterHub configuration running on a LAN or VPN. This will allow encrypted access to the server using https in a browser.
Note: JupyterHub with JupyterLab Install using Conda
This is a quick note about setting up a JupyterHub server and JupyterLab using conda with Anaconda Python.
HPC Parallel Performance for 3rd gen Threadripper, Xeon 3265W and EPYC 7742 (HPL HPCG Numpy NAMD)
On March 19, 2020 I did a webinar titled,
“AMD Threadripper 3rd Gen HPC Parallel Performance and Scaling ++(Xeon 3265W and EPYC 7742)”
The “++(Xeon 3265W and EPYC 7742)” part of that title was added after we had scheduled the webinar. It made the presentation a lot more interesting than the original Threadripper only title! This is a follow up post with the charts and plots of testing results presented in that webinar.
Threadripper 3990x vs 3970x Performance and Scaling (HPL, Numpy, NAMD plus GPUs)
Is 32-cores enough? I had some testing time again on an AMD Threadripper 32-core 3970x and thought it would be interesting to compare that to the 64-core 3990x. In this post I take a comparative look at parallel performance and scaling for HPL Linpack, Python numpy and the NAMD molecular dynamics program.
Threadripper 3990x 64-core Parallel Scaling
64 cores is a lot of cores! How well will parallel applications scale on that many cores? The answer, of course, is, it depends on the application. In this post I look at Amdhal’s Law parallel scaling for HPL Linpack, Python numpy and the NAMD molecular dynamics program.
Note: How To Install JupyterHub on a Local Server
This note describes installing and configuring JupyterHub and JupyterLab on a “bare-metal” server.
