Making Containers Easier with HPC Container Maker

Date and Time: 
Wednesday April 10th 2019
CG Auditorium
Craig Tierney
Containers simplify application deployments in the data centers by wrapping applications into an isolated virtual environment. By including all application dependencies like binaries and libraries, application containers run seamlessly in any data center environment. The HPC application containers available on NVIDIA GPU Cloud (NGC) dramatically improve ease of application deployment while delivering optimized performance. However, if the desired application is not available on NGC, building HPC containers from scratch trades one set of challenges for another. Parts of the software environment typically provided by the HPC data center must be redeployed inside the container. For those used to just loading the relevant environment modules, installing a compiler, MPI library, CUDA, and other core HPC components from scratch may be daunting. HPC Container Maker (HPCCM) is an open-source project that addresses the challenges of creating HPC application containers. HPCCM encapsulates into modular building blocks the best practices of deploying core HPC components with container best practices, to reduce container development effort, minimize image size, and take advantage of image layering. HPCCM makes it easier to create HPC application containers by separating the choice of what should go into a container image from the specification details of how to configure, build, and install a component. This separation also enables the best practices of HPC component deployment to transparently evolve over time.This talk will provide an overview of HPC Container Maker and discuss how the flexibility of HPCCM recipes and building blocks can simplify continuous integration testing.
Speaker Description: 
Craig Tierney is a Senior Solution Architect at NVIDIA supporting high performance computing (HPC) and deep learning (DL). His focus includes the architecture of GPU based systems to maximize HPC and DL performance and scalability. Prior to joining NVIDIA, Craig spent over 15 years providing high performance computing architecture and computational science support to NOAA and several other government and educational organizations including DOE, DOD, NASA and Stanford University. Craig holds a Ph.D. in Aerospace Engineering Sciences from the University of Colorado at Boulder.

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