NVIDIA GPU Cloud HPC Application Containers

OPTIMIZING HPC PERFORMANCE

Today’s groundbreaking scientific discoveries are taking place in high performance computing (HPC) data centers. However, installing and upgrading HPC applications on those shared systems comes with a set of unique challenges. Challenges that decrease accessibility, limit users to old features, and ultimately lower productivity.

The HPC application containers available on NVIDIA GPU Cloud (NGC) drastically improve ease of application deployment, while delivering optimized performance. 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.

HPC APPLICATION CONTAINERS AVAILABLE ON NGC TODAY

THREE REASONS WHY

Simplifying Application Deployment on a Cluster
Installing an HPC application requires that all libraries, compilers, and dependencies of the application are up to date and match with the cluster. Shared systems must support hundreds of applications with different requirements, making installation even more challenging. Containers eliminate the need to install the application, letting users easily run an HPC application on a cluster.
Accessing the Latest Features and Performance Improvements
The third-party managed HPC application containers for the latest application verisons provide additional features and better performance. Upgrading an application on a shared system is a challenge because the host may not have the right software stack required by the new version. Containers are agnostic to the underlying system, allowing users to take advantage of the application’s latest features and improved performance.
Running Applications on Any GPU Accelerated System
All the third-party managed HPC applications available through the NGC registry are designed to run on GPU-accelerated systems. Application users can log into NGC and pull the containers to run on a local workstation, GPU-accelerated HPC cluster, NVIDIA DGX Systems, and in the cloud. This kind of flexibility is changing the way HPC is used to make scientific breakthroughs.

Containers from NVIDIA GPU Cloud have automated application deployment, making users self-sufficient while allowing us to focus on other critical priorities. With the adoption of NGC, we’re able to help researchers with real problems.

– Ashwin Srinath, Research Facilitator, Clemson University IT Department

Containers from NVIDIA GPU Cloud have automated application deployment, making users self-sufficient while allowing us to focus on other critical priorities. With the adoption of NGC, we’re able to help researchers with real problems.

– Ashwin Srinath, Research Facilitator, Clemson University IT Department

Our cluster environment by necessity does not get updated fast enough to keep up with the requirements of the deep learning workflows. We made a significant investment in NVIDIA GPUs, and the NGC containers leverage that investment.

– Chris Reidy, Principal HPC System Admin, University of Arizona

Our cluster environment by necessity does not get updated fast enough to keep up with the requirements of the deep learning workflows. We made a significant investment in NVIDIA GPUs, and the NGC containers leverage that investment.

– Chris Reidy, Principal HPC System Admin, University of Arizona

GET ACCESS TO HPC APPLICATION CONTAINERS WITH NGC

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HPC Application Containers

OPTIMIZING HPC PERFORMANCE

HPC APPLICATION CONTAINERS AVAILABLE ON NGC TODAY

THREE REASONS WHY

Simplifying Application Deployment on a Cluster

Accessing the Latest Features and Performance Improvements

Running Applications on Any GPU Accelerated System

GET ACCESS TO HPC APPLICATION CONTAINERS WITH NGC