Applied Mathematics

Algorithms, methods and libraries that are fully scalable with full performance

DOE Program Manager
Karen Pao
DOE Office of Advanced Scientific Computing Research

A critical component of the future success of SciDAC is the development of new high-performance scalable numerical algorithms for core numerical components of scientific simulation, and the distribution of those algorithms through portable high-performance libraries.

Applied Mathematics Institutes Announced in September 2006

Combinatorial Scientific Computing and Petascale Simulations
Accelerating the development and deployment of fundamental enabling technologies in high performance computing
    Principal Investigator: Alex Pothen (apothen@purdue.edu)
    Purdue University

Applied Mathematics Centers Announced in September 2006

Advancing Science via Applied Mathematics
Applied Partial Differential Equations to develop simulation tools for solving multi-scale and multi-physics problems
    Principal Investigator: Phillip Colella (PColella@lbl.gov)
    Lawrence Berkeley National Laboratory

Bigger and Better Simulations
Interoperable technologies for advanced petascale simulations to improve accuracy and efficiency
    Principal Investigator: Lori Diachin (diachin2@llnl.gov)
    Lawrence Livermore National Laboratory

Improving Application Performance at the Petascale
Towards optimal petascale simulations
    Principal Investigator: David E. Keyes (kd2112@columbia.edu)
    Columbia University

Alumni Applied Mathematics Integrated Software Infrastructure Centers

An Algorithmic and Software Framework for Applied PDEs
Principal Investigator: Phil Colella, Lawrence Berkeley National Laboratory

Terascale Optimal PDE Solvers (TOPS)
Principal Investigator: David Keyes, Old Dominion University

Terascale Simulation Tools and Technologies (TSTT)
Principal Investigator: Jim Glimm, State University of New York, Stony Brook

 


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