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Read this article at https://www.pugetsystems.com/guides/1527
Dr Donald Kinghorn (Scientific Computing Advisor )

NVIDIA Docker2 with OpenGL and X Display Output

Written on July 11, 2019 by Dr Donald Kinghorn

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!


Read this article at https://www.pugetsystems.com/guides/1520
Dr Donald Kinghorn (Scientific Computing Advisor )

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.


Read this article at https://www.pugetsystems.com/guides/1477
Dr Donald Kinghorn (Scientific Computing Advisor )

How To Run Remote Jupyter Notebooks with SSH on Windows 10

Written on June 11, 2019 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1470
Dr Donald Kinghorn (Scientific Computing Advisor )

How To Use SSH Client and Server on Windows 10

Written on May 31, 2019 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1460
Dr Donald Kinghorn (Scientific Computing Advisor )

How To Install Docker and NVIDIA-Docker on Ubuntu 19.04

Written on May 7, 2019 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1419
Dr Donald Kinghorn (Scientific Computing Advisor )

How to Install TensorFlow with GPU Support on Windows 10 (Without Installing CUDA) UPDATED!

Written on April 26, 2019 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1405
Dr Donald Kinghorn (Scientific Computing Advisor )

How To Install CUDA 10.1 on Ubuntu 19.04

Written on April 5, 2019 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1386
Dr Donald Kinghorn (Scientific Computing Advisor )

TensorFlow Performance with 1-4 GPUs -- RTX Titan, 2080Ti, 2080, 2070, GTX 1660Ti, 1070, 1080Ti, and Titan V

Written 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.


Read this article at https://www.pugetsystems.com/guides/1370
Dr Donald Kinghorn (Scientific Computing Advisor )

Intel Xeon W-3175X and i9 9990XE Linpack and NAMD on Ubuntu 18.04

Written on February 28, 2019 by Dr Donald Kinghorn

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**.


Read this article at https://www.pugetsystems.com/guides/1345
Dr Donald Kinghorn (Scientific Computing Advisor )

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.


Read this article at https://www.pugetsystems.com/guides/1339
Dr Donald Kinghorn (Scientific Computing Advisor )

Numerical Computing Performance of 3 Intel 8-core CPUs - i9 9900K vs i7 9800X vs Xeon 2145W

Written on January 25, 2019 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1331
Dr Donald Kinghorn (Scientific Computing Advisor )

P2P peer-to-peer on NVIDIA RTX 2080Ti vs GTX 1080Ti GPUs

Written on January 11, 2019 by Dr Donald Kinghorn

There has been some concern about Peer-to-Peer (P2P) on the NVIDIA RTX Turing GPU's. P2P is not available over PCIe as it has been in past cards. It is available with very good performance when using NVLINK with 2 cards. I did some testing to see how the performance compared between the GTX 1080Ti and RTX 2080Ti. There were some interesting results!


Read this article at https://www.pugetsystems.com/guides/1321
Dr Donald Kinghorn (Scientific Computing Advisor )

AMD Threadripper and (1-4) NVIDIA 2080Ti and 2070 for NAMD Molecular Dynamics

Written on December 14, 2018 by Dr Donald Kinghorn

In my recent testing with the AMD Threadripper 2990WX is was impressed by the CPU based performance with the molecular dynamics program NAMD. NAMD makes a good benchmark for looking at CPU/GPU performance since it requires a balance and is usually limited by CPU. After some discussions I decided it would be good to look at multi-GPU performance with NAMD on Threadripper.


Read this article at https://www.pugetsystems.com/guides/1303
Dr Donald Kinghorn (Scientific Computing Advisor )

AMD Threadripper 2990WX 32-core vs Intel Xeon-W 2175 14-core - Linpack NAMD and Kernel Build Time

Written on December 6, 2018 by Dr Donald Kinghorn

I recently wrote a post about building and running AMD Threadripper 2990WX with HPL Linpack - a "How-To". Most of the time I had with the processor went into getting that to work. However, I did run a few other test jobs that I thought the 2990WX would do well with. I compared that against my personal workstation with a Xeon-W 2175. In this post I share those test runs with you. It's not thorough testing by any means but it was interesting and I was surprised a couple of times with the results.


Read this article at https://www.pugetsystems.com/guides/1291
Dr Donald Kinghorn (Scientific Computing Advisor )

How to Run an Optimized HPL Linpack Benchmark on AMD Ryzen Threadripper -- 2990WX 32-core Performance

Written on November 30, 2018 by Dr Donald Kinghorn

The AMD Ryzen Threadripper 2990WX with 32 cores is an intriguing processor. I've been asked about performance for numerical computing and decided to find out how well it would do with my favorite benchmark the "High Performance Linpack" benchmark. This is used to rank Supercomputers on the Top500 list. It is not always simple to run this test since it can require building a few libraries from source. This includes the all important BLAS library which AMD has optimized in their BLIS package. I give you a complete How-To guide for getting this running to see what the 2990WX is capable of.


Read this article at https://www.pugetsystems.com/guides/1267
Dr Donald Kinghorn (Scientific Computing Advisor )

RTX 2080Ti with NVLINK - TensorFlow Performance (Includes Comparison with GTX 1080Ti, RTX 2070, 2080, 2080Ti and Titan V)

Written on October 26, 2018 by Dr Donald Kinghorn

More Machine Learning testing with TensorFlow on the NVIDIA RTX GPU's. This post adds dual RTX 2080 Ti with NVLINK and the RTX 2070 along with the other testing I've recently done. Performance in TensorFlow with 2 RTX 2080 Ti's is very good! Also, the NVLINK bridge with 2 RTX 2080 Ti's gives a bidirectional bandwidth of nearly 100 GB/sec!


Read this article at https://www.pugetsystems.com/guides/1262
Dr Donald Kinghorn (Scientific Computing Advisor )

NVLINK on RTX 2080 TensorFlow and Peer-to-Peer Performance with Linux

Written on October 16, 2018 by Dr Donald Kinghorn

NVLINK is one of the more interesting features of NVIDIA's new RTX GPU's. In this post I'll take a look at the performance of NVLINK between 2 RTX 2080 GPU's along with a comparison against single GPU I've recently done. The testing will be a simple look at the raw peer-to-peer data transfer performance and a couple of TensorFlow job runs with and without NVLINK.


Read this article at https://www.pugetsystems.com/guides/1247
Dr Donald Kinghorn (Scientific Computing Advisor )

NVIDIA RTX 2080 Ti vs 2080 vs 1080 Ti vs Titan V, TensorFlow Performance with CUDA 10.0

Written on October 3, 2018 by Dr Donald Kinghorn

Are the NVIDIA RTX 2080 and 2080Ti good for machine learning? Yes, they are great! The RTX 2080 Ti rivals the Titan V for performance with TensorFlow. The RTX 2080 seems to perform as well as the GTX 1080 Ti (although the RTX 2080 only has 8GB of memory). I've done some testing using **TensorFlow 1.10** built against **CUDA 10.0** running on **Ubuntu 18.04** with the **NVIDIA 410.48 driver**.


Read this article at https://www.pugetsystems.com/guides/1236
Dr Donald Kinghorn (Scientific Computing Advisor )

How To Install CUDA 10 (together with 9.2) on Ubuntu 18.04 with support for NVIDIA 20XX Turing GPUs

Written on September 27, 2018 by Dr Donald Kinghorn

NVIDIA recently released version 10.0 of CUDA. This is an upgrade from the 9.x series and has support for the new Turing GPU architecture. This CUDA version has full support for Ubuntu 18.4 as well as 16.04 and 14.04. The CUDA 10.0 release is bundled with the new 410.x display driver for Linux which will be needed for the 20xx Turing GPU's. If you are doing development work with CUDA or running packages that require you to have the CUDA toolkit installed then you will probably want to upgrade to this. I'll go though how to do the install of CUDA 10.0 either by itself or along with an existing CUDA 9.2 install.


Read this article at https://www.pugetsystems.com/guides/1230
Dr Donald Kinghorn (Scientific Computing Advisor )

PyTorch for Scientific Computing - Quantum Mechanics Example Part 4) Full Code Optimizations -- 16000 times faster on a Titan V GPU

Written on September 14, 2018 by Dr Donald Kinghorn

This is the 16000 times speedup code optimizations for the scientific computing with PyTorch Quantum Mechanics example. The following quote says a lot, "The big magic is that on the Titan V GPU, with batched tensor algorithms, those million terms are all computed in the same time it would take to compute 1!!!"


Read this article at https://www.pugetsystems.com/guides/1225
Dr Donald Kinghorn (Scientific Computing Advisor )

PyTorch for Scientific Computing - Quantum Mechanics Example Part 3) Code Optimizations - Batched Matrix Operations, Cholesky Decomposition and Inverse

Written on August 31, 2018 by Dr Donald Kinghorn

An amazing result in this testing is that "batched" code ran in constant time on the GPU. That means that doing the Cholesky decomposition on 1 million matrices took the same amount of time as it did with 10 matrices! In this post we start looking at performance optimization for the Quantum Mechanics problem/code presented in the first 2 posts. This is the start of the promise to make the code over 15,000 times faster! I still find the speedup hard to believe but it turns out little things can make a big difference.


Read this article at https://www.pugetsystems.com/guides/1222
Dr Donald Kinghorn (Scientific Computing Advisor )

PyTorch for Scientific Computing - Quantum Mechanics Example Part 2) Program Before Code Optimizations

Written on August 16, 2018 by Dr Donald Kinghorn

This is the second post on using Pytorch for Scientific computing. I'm doing an example from Quantum Mechanics. In this post we go through the formulas that need to coded and write them up in PyTorch and give everything a test.


Read this article at https://www.pugetsystems.com/guides/1207
Dr Donald Kinghorn (Scientific Computing Advisor )

Doing Quantum Mechanics with a Machine Learning Framework: PyTorch and Correlated Gaussian Wavefunctions: Part 1) Introduction

Written on July 31, 2018 by Dr Donald Kinghorn

A Quantum Mechanics problem coded up in PyTorch?! Sure! Why not? I'll explain just enough of the Quantum Mechanics and Mathematics to make the problem and solution (kind of) understandable. The focus is on how easy it is to implement in PyTorch. This first post will give some explanation of the problem and do some testing of a couple of the formulas that will need to be coded up.


Read this article at https://www.pugetsystems.com/guides/1196
Dr Donald Kinghorn (Scientific Computing Advisor )

NAMD Custom Build for Better Performance on your Modern GPU Accelerated Workstation -- Ubuntu 16.04, 18.04, CentOS 7

Written on July 20, 2018 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1193
Dr Donald Kinghorn (Scientific Computing Advisor )

Why You Should Consider PyTorch (includes Install and a few examples)

Written on July 13, 2018 by Dr Donald Kinghorn

PyTorch is a relatively new ML/AI framework. It combines some great features of other packages and has a very "Pythonic" feel. It has excellent and easy to use CUDA GPU acceleration. It is fun to use and easy to learn. read on for some reasons you might want to consider trying it. I've got some unique example code you might find interesting too.