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NVIDIA Simplifies Building Containers for HPC Applications

In this video, CJ Newburn from NVIDIA describes how users can benefit from running their workloads in the NVIDIA GPU Cloud. “A container essentially creates a self contained environment. Your application lives in that container along with everything the application depends on, so the whole bundle is self contained. NVIDIA is now offering a script as part of an open source project called HPC Container Maker, or HPCCM that makes it easy for developers to select the ingredients they want to go into a container, to provide those ingredients in an optimized way using best-known recipes.”

Why Use Containers for HPC on the NVIDIA GPU Cloud?

“Containers just make life easy. So one of the things that people have issues with running bare metal is from time to time their libraries change, maybe they want to try the new version of CUDA, the new version of cuDNN, and they forget to simlink it back to the original path. If you have packaged your app into a container, it’s the same every time.”

Why UIUC Built HPC Application Containers for NVIDIA GPU Cloud

In this video from the GPU Technology Conference, John Stone from the University of Illinois describes how container technology in the NVIDIA GPU Cloud help the University distribute accelerated applications for science and engineering. “Containers are a way of packaging up an application and all of its dependencies in such a way that you can install them collectively on a cloud instance or a workstation or a compute node. And it doesn’t require the typical amount of system administration skills and involvement to put one of these containers on a machine.”

New HP Z8 is “World’s Most Powerful Workstation for Machine Learning Development”

HP Z Workstations, with new NVIDIA technology, are ideal for local processing at the edge of the network – giving developers more control, better performance and added security over cloud-based solutions. “Products like the HP Z8, the most powerful workstation for ML development, coupled with the new NVIDIA Quadro GV100, the HP ML Developer Portal and our expanded services offerings will undoubtedly fast-track the adoption of machine learning.”

Video: Deploy HPC Applications Faster with NVIDIA GPU Cloud

The HPC application containers available on NVIDIA GPU Cloud (NGC) drastically improve ease of application deployment, while delivering optimized performance. The containers include HPC applications such as NAMD, GROMACS, and Relion. NGC gives researchers and scientists the flexibility to run HPC application containers on NVIDIA Pascal­ and NVIDIA Volta-powered systems including Quadro-powered workstations, NVIDIA DGX Systems, and HPC clusters.

NVIDIA Makes Visualization Easier in the Cloud

Visualizing the results of a simulation can give new insight into complex scientific problems. Interactive viewing of entire datasets can lead to earlier understanding of the challenge at hand and can enhance the understanding of complex phenomena. With the release of the of HPC Visualization Containers with the NVIDIA CPU Cloud, it has become much easier to get a visualization system up and production ready much quicker than ever before.

Consumer GPUs come to NVIDIA GPU Cloud for AI Research

Today NVIDIA announced that hundreds of thousands of AI researchers using desktop GPUs can now tap into the power of NVIDIA GPU Cloud. “With GPU-optimized software now available to hundreds of thousands of researchers using NVIDIA desktop GPUs, NGC will be a catalyst for AI breakthroughs and a go-to resource for developers worldwide.”

NVIDIA GPU Cloud comes to AWS with Volta GPUs

“In just a few steps, the NVIDIA GPU Cloud (NGC) container registry helps developers get started with no-cost access to a comprehensive, easy-to-use, fully optimized deep learning software stack. The cloud-based service is available immediately to users of the just-announced Amazon Elastic Compute Cloud (Amazon EC2) P3 instances featuring NVIDIA Tesla V100 GPUs. NVIDIA plans to expand support to other cloud platforms soon.”