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.)
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.
The, much anticipated, AMD Ryzen 3950x 16-core processor is out! As always the first thing I wanted know was the double precision floating point performance. My two favorite applications for a “first look” at a new CPU are Linpack and NAMD.
In this post I’ve done more testing with Ryzen 3900X looking at the effect of BLAS libraries on a simple but computationally demanding problem with Python numpy. The results may surprise you! I start with a little bit of history of Intel vs AMD performance to give you what may be a new perspective on the issue.
I was able to spend a little time with an AMD Ryzen 3900X. Of course the first thing I wanted know was the double precision floating point performance. My two favorite applications for a “first look” at a new processor are Linpack and NAMD. The Ryzen 3900X is a pretty impressive processor!
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