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.
How To Use MKL with AMD Ryzen and Threadripper CPU's (Effectively) for Python Numpy (And Other Applications)Written on November 27, 2019 by Dr Donald Kinghorn
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.
It's time for a "Docker with NVIDIA GPU support" update. This post will guide you through a useful Workstation setup (including User-name-spaces and performance tuning) with the new versions of Docker and the NVIDIA GPU container toolkit.
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.
This is a short post showing a performance comparison with the RTX2070 Super and several GPU configurations from recent testing. The comparison is with TensorFlow running a ResNet-50 and Big-LSTM benchmark.
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!
Docker is a great Workstation tool. It is mostly used for command-line application or servers but, ... What if you want to run an application in a container, AND, use an X Window GUI with it? What if you are doing development work with CUDA and are including OpenGL graphic visualization along with it? You CAN do that!
Install TensorFlow 2 beta1 (GPU) on Windows 10 and Linux with Anaconda Python (no CUDA install needed)Written on June 26, 2019 by Dr Donald Kinghorn
TensorFlow 2.0.0-beta1 is available now and ready for testing. What if you want to try it but don't want to mess with doing an NVIDIA CUDA install on your system. The official TensorFlow install documentations has you do that, but it's really not necessary.
Being able to run Jupyter Notebooks on remote systems adds tremendously to the versatility of your workflow. In this post I will show a simple way to do this by taking advantage of some nifty features of secure shell (ssh). What I'll do is mostly OS independent but I am putting an emphasis on Windows 10 since many people are not familiar with tools like ssh on that OS.
This post is a setup guide and introduction to ssh client and server on Windows 10. Microsoft has a native OpenSSH client AND server on Windows. They are standard (and in stable versions) on Windows 10 since the 1809 "October Update". This guide should helpful to both Windows and Linux users who want better interoperability.
Being able to get Docker and the NVIDIA-Docker runtime working on Ubuntu 19.04 makes this new and (currently) mostly unsupported Linux distribution a lot more useful. In this post I'll go through the steps that I used to get everything working nicely.
This post is the needed update to a post I wrote nearly a year ago (June 2018) with essentially the same title. This time I have presented more details in an effort to prevent many of the "gotchas" that some people had with the old guide. This is a detailed guide for getting the latest TensorFlow working with GPU acceleration without needing to do a CUDA install.
Ubuntu 19.04 will be released soon so I decided to see if CUDA 10.1 could be installed on it. Yes, it can and it seems to work fine. In this post I walk through the install and show that docker and nvidia-docker also work. I ran TensorFlow 2.0- alpha on Ubuntu 19.04 beta.
TensorFlow Performance with 1-4 GPUs -- RTX Titan, 2080Ti, 2080, 2070, GTX 1660Ti, 1070, 1080Ti, and Titan VWritten on March 14, 2019 by Dr Donald Kinghorn
I have updated my TensorFlow performance testing. This post contains up-to-date versions of all of my testing software and includes results for 1 to 4 RTX and GTX GPU's. It gives a good comparative overview of most of the GPU's that are useful in a workstation intended for machine learning and AI development work.
There are 2 recent Intel processors that are really strange, the Xeon W-3175X 28-core, and the Core i9 9990XE overclocked 14-core. I was able to get a little time in on the these processors. I ran a couple of numerical compute performance tests with the Intel MKL Linpack benchmark and NAMD. I used the same system image that I had used recently to look at 3 Intel 8-core processors so I will include those results here as well. **There will be results for W-3175, 9990XE, 9800X, W-2145, and 9900K**.
RTX Titan TensorFlow performance with 1-2 GPUs (Comparison with GTX 1080Ti, RTX 2070, 2080, 2080Ti, and Titan V)Written on January 30, 2019 by Dr Donald Kinghorn
I've done some testing with 2 NVIDIA RTX Titan GPU's running machine learning jobs with TensorFlow. The RTX Titan is a great card but there is good news and bad news.
In this post I'll take a brief look at the numerical computing performance of three very capable 8-core processors -- i9 9900K, i9 9800X and Xeon 2145W All three are great CPU's but there are some significant differences that can cause confusion. I'll discuss these differences and see how the processors stack up when running Linpack and NAMD molecular dynamics simulations.