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.
This note describes installing and configuring JupyterHub and JupyterLab on a “bare-metal” server.
64 cores! The latest AMD Threadripper is out, the 3990x 64-core. I’ve spent the last couple of days running benchmarks and have some results showing raw numerical compute performance using my standard CPU testing applications HPL Linpack and the molecular dynamics program NAMD. The 3990x is a great processor with exceptional performance. Especially for NAMD! (There were some difficulties and disappointments during the testing and I report those here too.)
This note describes creating an ISO image for a fully automatic, customized Ubuntu 18.04 server install.
How to setup PowerShell nicely for using git with command completion and color highlighted shell prompt git status/action notifications.
Starting 2020 off with an addition to my writing, “micro blogging” via GitHub Gists
It’s the end of the 2010’s and start of 2020’s. Time to reflect …
The Super Computing conference annual US counterpart is always a great meeting. It’s a chance to see the trend and get sentiment for the highest performance end of computing. I have written up a few observations and provided a few interesting links for SC19.
In this post I’m going to show you a simple way to significantly speedup Python numpy compute performance on AMD CPU’s when using Anaconda Python.
AMD Threadripper 3970x 32-core! …The, third new AMD processor I’ve had the pleasure of trying recently. I’m running it through the same double precision floating point performance tests as the recently tested Ryzen processors, Linpack and NAMD.
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