HPC for Data Analytics

This page is under construction.

With the HPC-DA system, the TU Dresden provides infrastructure for High-Performance Computing and Data Analytics (HPC-DA) for German researchers for computing projects with focus in one of the following areas:

• machine learning scenarios for large systems
• evaluation of various hardware settings for large machine learning problems, including accelerator and compute node configuration and memory technologies
• processing of large amounts of data on highly parallel machine learning infrastructure.

Currently we offer 25 Mio core hours compute time per year for external computing projects. Computing projects have a duration of up to one year with the possibility of extensions, thus enabling projects to continue seamlessly. Applications for regular projects on HPC-DA can be submitted at any time via the online web-based submission and review system. The reviews of the applications are carried out by experts in their respective scientific fields. Applications are evaluated only according to their scientific excellence.

ZIH provides a portfolio of preinstalled applications and offers support for software installation/configuration of project-specific applications. In particular, we provide consulting services for all our users, and advise researchers on using the resources in an efficient way

Access

• Application for access using this Online Web Form

Hardware Overview

• Nodes for machine learning (Power9)
• NVMe Storage (2 PB)
• Warm archive (10 PB)
• HPC nodes (x86) for DA (island 6)
• Compute nodes with high memory bandwidth: AMD Rome Nodes (island 7)

File Systems and Object Storage

• Lustre
• BeeGFS
• Quobyte
• S3

HOWTOS

• Get started with HPC-DA
• IBM Power AI
• Work with Singularity Containers on Power9
  
• TensorFlow on HPC-DA (native)
• Tensorflow on Jupyter notebook
• Create and run your own TensorFlow container for HPC-DA (Power9)
• TensorFlow on x86
• PyTorch on HPC-DA (Power9)
• Python on HPC-DA (Power9)
• JupyterHub
• R on HPC-DA (Power9)
• Big Data frameworks: Apache Spark, Apache Flink, Apache Hadoop

-- Main.UlfMarkwardt - 2019-05-05