Data Management

We present a work-in-progress snapshot of learning with a 15 billion parameter deep learning network on HPC architectures applied to the largest publicly available natural image and video dataset released to-date. Recent advancements in unsupervised deep neural networks suggest that scaling up such networks in both model and training dataset size can yield significant improvements in the learning of concepts at the highest layers.

Useful introductory information on LC's software environment is presented in the Software and Development Environment section of the Introduction to Livermore Computing Resources, or the Linux Clusters Overview for system-specific information.

This page lists available online tutorials related to parallel programming and using LC's HPC systems.

Our data management area provides users with a variety of powerful and time-conserving ways to access, search, transfer, and archive large-scale scientific data.The visualization team develops and supports tools for visualizing and presenting scientific data generated by users of the LC high-performance computing Center.