Accelerated Data Analytics and Computing Institute


The Accelerated Data Analytics and Computing Institute has been established to explore potential future collaboration among Oak Ridge National Laboratory, the Swiss Federal Institute of Technology, Zurich (ETH/CSCS), Tokyo Institute of Technology, Argonne National Laboratory, CSC – IT Center for Science, Forschungszentrum Jülich, Lawrence Livermore National Laboratory, The National Computational Infrastructure (NCI) of the Australian National University, RIKEN Center for Computational Science, The University of Tokyo’s Information Technology Center’s Supercomputing Research Division, and National Institute of Advanced Industrial Science and Technology, Department of Information Technology and Human Factors. Consistent with their respective missions, the Participants seek to collaborate and leverage their respective investments in application software readiness in order to expand the breadth of applications capable of running on accelerated architectures. All three organizations manage HPC centers that run large, GPU-accelerated supercomputers and provide key HPC capabilities to academia, government, and industry to solve many of the world’s most complex and pressing scientific problems.

Focus Areas & Charge Questions

ADAC will focus on multiple objectives spanning performance, hardware, and applications, including:

  • Adapting important scientific and engineering applications to hybrid accelerated architectures.
  • Partnering with HPC vendors to evaluate architecture diversity.
  • Enabling collaborative scientific efforts in hybrid accelerated data and compute.
  • Ensuring sustainability and portability of critical applications.
  • Sharing best practices regarding the operation, management, and procurement of HPC resources.

The Governance Committee proposed the following charge questions for ADAC12:

  • Applications: Focus on the optimal heterogeneity and use of the heterogeneity to maximize the scientific output
  • Performance Portability: Focus on the portability across the heterogeneous components and the ability to measure the increased output in some meaningful way (performance)
  • Resource Management: Focus on workflows to enable maximum impact by the heterogeneous elements