TensorFlow is on it’s way to becoming the “standard” framework for machine learning. There are many reasons for that, and, it is not just for machine learning! In this post I’ll give a descriptive introduction to TensorFlow. This is the first post in a series on how to work with TensorFlow. Hopefully after reading thsi you will have a better understanding of the What? and Why? of TensorFlow.
It’s been a few years since a game caught the interest of my family.
I worked in sales at Puget Systems when I began hearing customers mention a game called Minecraft. The simple, blocky nature of the game carried over to the hardware requirements. Minecraft didn’t require a high-end gaming rig. My three oldest couldn’t get enough of building homes, trying to stay alive and setting anything they could find on fire with lava.
Linux is often considered the operating system of choice for power users, but does DaVinci Resolve actually perform any better in it than in Windows? Even if it does, is it enough to compensate for Linux’s much higher learning curve?
Puget Systems will be exhibiting at this year’s GPU Technology Conference, March 26th – 29th. We will be displaying our new GPU accelerated workstations including those for photogrammetry, rendering, virtual reality and machine learning. Come meet with us at Booth #705!
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.
After Effects is a tricky application when it comes to choosing a CPU as there are many factors that come into play. Not only is there raw rendering performance, but the new integration with Cinema4D and even the amount of system RAM you need all play a role in determining what CPU is the ideal choice for your workflow.
Following up on our previous article about SOLIDWORKS 2018 GPU performance, we have been provided with an extremely complex assembly that finally shows some performance difference between low- and high-end video cards within the same family. Armed with this 4372 part, 40.9 million triangle model we ran through testing on multiple Quadro and Radeon Pro graphics cards to see how they handle such a monstrously large project.
Blackmagic’s DaVinci Resolve is known for how well it utilizes multiple GPUs to improve performance, but is this still true with cards like the new NVIDIA Titan V? And do you really need a Xeon or Dual Xeon setup to get the best performance possible?
Designed and built specifically for professional workstations, NVIDIA Quadro GPUs power more than 200 professional applications across a broad range of industries including manufacturing, media and entertainment, sciences, and energy. Professionals trust them to realize their most ambitious visions
In this post I present some Multi-GPU scaling tests running TensorFlow on a very nice system with 8 1080Ti GPU’s. I use the Docker Workstation setup that I have recently written about. The job I ran for this testing was the “Billion Words Benchmark” using an LSTM model. Results were very good and better than expected.
Puget Systems builds custom workstations, servers and storage solutions tailored for your work.
We provide:
Extensive performance testing
making you more productive and giving better value for your money
Reliable computers
with fewer crashes means more time working & less time waiting
Support that understands
your complex workflows and can get you back up & running ASAP
A proven track record
as shown by our case studies and customer testimonials
