Linux

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

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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.

Microsoft Build 2018 — impressions

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I attended the Microsoft Build 2018 developers conference this week and really enjoyed it. I wanted to share my “big picture” feelings about it and some of the things that stood out to me. I’m not going to give you a “reporters” view or repeat press-release items. This is just my personal impression of the conference.

NAMD Performance on Xeon-Scalable 8180 and 8 GTX 1080Ti GPUs

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This post will look at the molecular dynamics program, NAMD. NAMD has good GPU acceleration but is heavily dependent on CPU performance as well. It achieves best performance when there is a proper balance between CPU and GPU. The system under test has 2 Xeon 8180 28-core CPU’s. That’s the current top of the line Intel processor. We’ll see how many GPU’s we can add to those Xeon 8180 CPU’s to get optimal CPU/GPU compute balance with NAMD.

How-To Setup NVIDIA Docker and NGC Registry on your Workstation – Part 5 Docker Performance and Resource Tuning

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This should be the last post in this series dealing with the Docker setup for accessing the NVIDIA NCG Docker registry on your workstation. There are a couple of configuration tuning changes that you may want to make. These will improve performance and ensure that you have proper system “user limit” resources to handle large application and job runs with docker.

How-To Setup NVIDIA Docker and NGC Registry on your Workstation – Part 4 Accessing the NGC Registry

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This post will go through how to get access to the NVIDIA NGC container registry on your workstation. The first 3 posts in this series gave instructions on how to install and configure a base Ubuntu 16.04 workstation system with Docker and NVIDIA-Docker for a usable work-flow. With that taken care of we can get setup to use the many useful docker images in the NGC container registry for your local system.

How-To Setup NVIDIA Docker and NGC Registry on your Workstation – Part 3 Setup User-Namespaces

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In this post I’ll go through setting up Docker to use User-Namespaces. This is a very important step to achieving a comfortable docker work-flow on a personal Workstation. I will show you how to configure Docker so that instead of files and processes being owned by root they will be owned by your personal user account. This will make using Docker containers on your system safer and feel much the same as a “normally” installed application.

How-To Setup NVIDIA Docker and NGC Registry on your Workstation – Part 1 Introduction and Base System Setup

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One of my New Years resolutions was to adopt a Docker based workflow. I had also promised in my recent post on testing the Titan V that I would do a series of How-To’s on setting up docker and ultimately configuring and using the excellent NVIDIA NGC docker registry. This is the fist post of that series and covers the base system setup, motivation and references.

Intel CPU flaw kernel patch effects – GPU compute Tensorflow Caffe and LMDB database creation

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The Intel CPU flaw and the Meltdown and Spectre security exploits are causing a lot of concern. There is a possibility of application slowdown from the kernel patches to mitigate the exploits. This slowdown concern is a concern for GPU accelerated application because of the systems calls they require for moving data between CPU and GPU memory space. I did some testing on a couple of large Tensorflow and Caffe machine learning jobs along with the creation of a LMDA database from 1.3 million images.