I was preparing a Puget Systems Traverse Skylake based laptop for GPU accelerated molecular dynamics demos at the upcoming ACS meeting and decided to see if I could get Ubuntu 16.04 beta working with NVIDIA CUDA 7.5. It worked!
Windows 10 with Xeon Phi
Can you use an Intel Xeon Phi with Windows 10? Yes, you can. However, just because you can do something, doesn’t mean that you should do it! I did a set up and a little testing mainly just to see if it would work — it does!
Molecular Dynamics Performance on GPU Workstations — NAMD
Molecular Dynamics programs can achieve very good performance on modern GPU accelerated workstations giving job performance that was only achievable using CPU compute clusters only a few years ago. The group at UIUC working on NAMD were early pioneers of using GPU’s for compute acceleration and NAMD has very good performance acceleration using NVIDIA CUDA. We show you how good that performance is on modern Nvidia GPU’s
OpenACC for free! — NVIDIA OpenACC Toolkit
NVIDIA and PGI are offering “PGI Accelerator with OpenACC” free to academia (or 90 day trial for commercial users) under the banner “NVIDIA OpenACC Toolkit”. It’s about time!
Xeon Phi 5110p and Free Intel Parallel Studio Cluster Edition
Another amazing deal on Xeon Phi from Intel! This time you can get a 90% discount on a Phi 5110p and get the Intel Parallel Studio Cluster edition with a 1 year license for free.
GTX 980 Ti Linux CUDA performance vs Titan X and GTX 980
NVIDIA has just launched the GTX 980 Ti and I got to run some benchmarks on one. How is the Linux CUDA performance? Almost as good as the TitanX! This is another great card from NVIDIA for single precision compute loads. We’ve got some number to show it.
Install NVIDIA CUDA on Fedora 22 with gcc 5.1
Fedora 22 is full of new goodness like kernel 4.0 and gcc 5.1 and yes, you can install and run CUDA on it! It’s not officially supported but I did manage to get it working!
5 Ways of Parallel Programming
Modern computing hardware is all about parallelism. This is because we essentially hit the wall several years ago on increasing core clock frequency to speedup serial code execution. The transistor count has continued to follow Moore’s Law (doubling every 1.5-2 years) but these transistors have mostly gone into multiple cores, vector units, memory controllers, etc. on a single die. To utilize this hardware, software needs to be written to take advantage of it, i.e. you have to go parallel.
GTC 2015 Deep Learning and OpenPOWER
Another great GTC meeting. NVIDIA does this right! The most interesting aspects for me this year were the talks on “Deep Learning” (Artificial Neural Networks) and OpenPOWER. I have some observations and links to recordings of the keynotes and talks. Enjoy!
NVIDIA CUDA GPU computing on a (modern) laptop
Modern high-end laptops can be treated as desktop system replacements so it’s expected that people will want to try to do some serious computing on them. Doing GPU accelerated computing on a laptop is possible and performance can be surprisingly good with a high-end NVIDIA GPU. [I’m looking at GTX 980m and 970m ]. However, first you have to get it to work! Optimus technology can present serious problems to someone who wants to run a Linux based CUDA laptop computing platform. Read on to see what worked.
