In this third post on Docker and Nvidia-Docker we will configure “kernel user namespaces” for use with Docker. This will increase the security and usability of Docker on your desktop. This is a relatively new feature in Docker and is a key component for the viability of “Docker on your workstation”.
In this second post on Docker and Nvidia-Docker we will do an install and setup on an Ubuntu 16.04 workstation.
Docker containers together with the NVIDIA-docker can provide relief from dependency and configuration difficulties when setting up GPU accelerated machine learning environments on a workstation. In this post I will discuss motivation for considering this.
NVIDIA has released the Quadro GP100 bringing Tesla P100 Pascal performance to your desktop. This new card gives you the compute performance of the NVIDIA Tesla P100 together with Quadro display capability. That means full double precision floating point capability of the P100 and NVLINK for multiple cards.
Does PCIe X16 give better performance than X8 for training models with Caffe when using cuDNN? Yes, but not by much!
The new NVIDIA GeForce GTX 1080 and GTX 1070 GPU’s are out and I’ve received a lot of questions about NAMD performance. The short answer is — performance is great! I’ve got some numbers to back that up below. We’ve got new Broadwell Xeon and Core-i7 CPU’s thrown into the mix too. The new hardware refresh gives a nice step up in performance.
A brief description of graphics driver Timeout Detection and Recovery, why it can be problematic for intensive GPU codes, and how to work around it so that Windows can be a viable GPU computing platform.
Machine Learning is getting a lot of attention these days and with good reason. There are mountains of data to work with and computing resources to handle the problems are easily attainable. Even a single GPU accelerated workstation is capable of serious work.
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
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