Research

Computer Science

The Computer Science research agenda fills a critical gap in scientific computing. The computing resources required to fulfill the Office of Science mission exceed the state-of-the-art by a significant margin. Furthermore, the software tools, libraries and the distributed software environments needed to accelerate scientific discovery through modeling and simulation are beyond the realm of commercial interest. Yet, the computing resources and the applications that run on them are vital to maintaining the United States' competitiveness in the world economy.

The Computer Science program supports research that enables computing at extreme scales and the understanding of extreme scale data from both simulations and experiments. It aims to make scientific computers as easy and effective to use as possible. Extreme scale refers to the use of Exascale computing platforms that will operational in the 2018-2020 timeframe. Exascale computing platforms will be capable of up to 1 quintillion (10^18) floating point operations per second.

In order to ensure the efficiency and productivity of the supercomputing systems managed and operated by the Office of Science, the Computer Science program addresses challenges in advanced computer architectures; programming models, languages, and compilers; execution models, operating, runtime, and file systems; performance and productivity tools; and data management and data analytics, including visual analysis, as described at this link.

 

ASCR is Pleased to Announce:

Nine new awards under Scientific Data Management, Analysis & Visualization at Extreme Scale 2
  • Usable Data Abstractions for Next-Generation Scientific Workflows
    • PI: Deb Agarwal, LBNL, with ORNL, University of California Berkeley, and University of Washington
  • Optimizing the Energy Usage and Cognitive Value of Extreme Scale Data Analysis Approaches
    • PI: Jim Ahrens, LANL, with Virginia Tech, University of New Hampshire, and University of Texas Austin (2 awards)
  • Scalable Analysis Methods and In Situ Infrastructure for Extreme Scale Knowledge Discovery
    • PI: Wes Bethel, LBNL, with ANL, Georgia Tech, JMSI, Inc., and Kitware, Inc.
  • A Unified Data-Driven Approach for Programming In Situ Analysis and Visualization
    • PI: Pat McCormick, LANL, with SNL, University of Utah, Stanford University, and Kitware, Inc.
  • XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem
    • PI: Ken Moreland, Sandia, with ORNL, LANL, University of California Davis, University of Oregon, and Kitware, Inc.
  • High-Performance Decoupling of Tightly Coupled Data Flows
    • PI: Tom Peterka, ANL, and SNL
  • In Situ Indexing and Query Processing of AMR Data
    • PI: Nagiza Samatova, NCSU, with LBNL
  • Performance Understanding and Analysis for Exascale Data Management Workflows
    • PI: Karsten Schwan, Georgia Tech, with ORNL and University of Oregon
  • Extreme-Scale Distribution-Based Data Analysis
    • PI: Han-Wei Shen, Ohio State University, with ANL and LANL
CS Portfilio

 

 

 

Additional Workshop Reports

 

previous Computer Science award announcements

Last modified: 8/27/2014 9:35:52 AM