In this post I will show you how to install NVIDIA’s build of TensorFlow 1.15 into an Anaconda Python conda environment. This is the same TensorFlow 1.15 that you would have in the NGC docker container, but no docker install required and no local system CUDA install needed either.
Quad RTX3090 GPU Power Limiting with Systemd and Nvidia-smi
This is a follow up post to “Quad RTX3090 GPU Wattage Limited “MaxQ” TensorFlow Performance”. This post will show you a way to have GPU power limits set automatically at boot by using a simple script and a systemd service Unit file.
Quad RTX3090 GPU Wattage Limited “MaxQ” TensorFlow Performance
Can you run 4 RTX3090’s in a system under heavy compute load? Yes, by using nvidia-smi I was able to reduce the power limit on 4 GPUs from 350W to 280W and achieve over 95% of maximum performance. The total power load “at the wall” was reasonable for a single power supply and a modest US residential 110V, 15A power line.
RTX3070 (and RTX3090 refresh) TensorFlow and NAMD Performance on Linux (Preliminary)
The GeForce RTX3070 has been released.
The RTX3070 is loaded with 8GB of memory making it less suited for compute task than the 3080 and 3090 GPUs. we have some preliminary results for TensorFlow, NAMD and HPCG.
RTX3090 TensorFlow, NAMD and HPCG Performance on Linux (Preliminary)
The second new NVIDIA RTX30 series card, the GeForce RTX3090 has been released.
The RTX3090 is loaded with 24GB of memory making it a good replacement for the RTX Titan… at significantly less cost! The performance for Machine Learning and Molecular Dynamics on the RTX3090 is quite good, as expected.
RTX3080 TensorFlow and NAMD Performance on Linux (Preliminary)
The much anticipated NVIDIA GeForce RTX3080 has been released. How good is it with TensorFlow for machine learning? How about molecular dynamics with NAMD? I’ve got some preliminary numbers for you!
HPC Parallel Performance for 3rd gen Threadripper, Xeon 3265W and EPYC 7742 (HPL HPCG Numpy NAMD)
On March 19, 2020 I did a webinar titled,
“AMD Threadripper 3rd Gen HPC Parallel Performance and Scaling ++(Xeon 3265W and EPYC 7742)”
The “++(Xeon 3265W and EPYC 7742)” part of that title was added after we had scheduled the webinar. It made the presentation a lot more interesting than the original Threadripper only title! This is a follow up post with the charts and plots of testing results presented in that webinar.
Threadripper 3990x vs 3970x Performance and Scaling (HPL, Numpy, NAMD plus GPUs)
Is 32-cores enough? I had some testing time again on an AMD Threadripper 32-core 3970x and thought it would be interesting to compare that to the 64-core 3990x. In this post I take a comparative look at parallel performance and scaling for HPL Linpack, Python numpy and the NAMD molecular dynamics program.
Threadripper 3990x 64-core Parallel Scaling
64 cores is a lot of cores! How well will parallel applications scale on that many cores? The answer, of course, is, it depends on the application. In this post I look at Amdhal’s Law parallel scaling for HPL Linpack, Python numpy and the NAMD molecular dynamics program.
AMD Threadripper 3990x 64-core Linpack and NAMD Performance (Linux)
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.)