FASTMath SciDAC Institute
Application Partnerships

Efforts in the first decade of the SciDAC program have conclusively demonstrated that significant advances in science can be realized through close and sustained interactions among domain scientists and applied mathematicians. Examples enabled by members of the FASTMath team include

  1. advances in parallel sparse eigenvalue calculations enabling the prediction of the exotic nucleus^14F, preceding observations in experiments;
  2. the development of adaptive curved mesh techniques and high order methods applied to accelerator modeling leading to significantly improved accelerator designs; and
  3. the development of scalable structured adaptivemesh refinement methods for low-Mach number reacting flows enabling high-fidelity predictive simulations in turbulent flames. 

The FASTMath SciDAC Institute is building on this success and provides the mathematical algorithms, software tools, and human expertise to enable effective use of the high-end computing facilities by Department of Energy and other application scientists.

As the complexity of computer architectures and the range of physical phenomena that can be numerically simulated for important DOE applications continue to grow, application scientists have two fundamental challenges to overcome. First, they must continue to improve the quality of their simulations by increasing accuracy and fidelity of the solution and improving the robustness and reliability of both their software and their algorithms. Second, they must adapt their computations to make effective use of the high-end computing facilities being acquired by DOE over the next five years. This challenge necessitates million-way parallelism and implementations that are efficient on many-/multi-core nodes. The FASTMath SciDAC Institute is working with DOE application scientists to address both of these challenges by focusing on the interactions among mathematical algorithms, software design, and computer architectures.

The FASTMath team partners with DOE applications scientists from across the Office of Science, the NNSA, and the applied energy offices.  In each case we give a brief overview of our collaborations and progress in advancing scientific goals. More information can be obtained by contacting the indicated FASTMath team members.

Office of Biological and Environmental Research

The U.S. Department of Energy’s Office of Biological and Environmental Research (BER) conducts research in the areas of climate and environmental sciences and biological systems science. Partnerships in climate and environmental sciences aim to advance the simulation and predictive capabilities of state-of-science climate modeling and provide improved models for better understanding the movement of subsurface contamination. Partnerships in biological systems seek to develop new methods for modeling complex biological systems, including molecular complexes, metabolic and signaling pathways, individual cells, and ultimately, interacting organisms and ecosystems.

Applications

The ACES4BGC project is advancing the predictive capabilities of Earth System Models by reducing two of the largest sources of uncertainty, aerosols and biospheric feedbacks, with a highly efficient computational approach.

The MULTISCALE project aims to produce better climate models to serve as the scientific tools and predictive tools that will address the needs of both the climate sciences and policy-oriented communities.

The PISCEES project is enabling quantitative predictions of coupled ice-sheet/climate evolution using a new generation of high-performance computers and computational tools.

Office of Basic Energy Sciences

The Department of Energy’s Office of Basic Energy Sciences (BES) supports fundamental research to understand, predict, and ultimately control matter and energy at the electronic, atomic, and molecular levels, to provide the foundations for new energy technologies and to support DOE missions in energy, environment, and national security. Through SciDAC partnerships, BES is developing new algorithms and computational approaches which could dramatically accelerate the discovery of new materials and processes as well as provide fundamental understanding and improvement of current materials and processes. Implementing these new algorithms on current and next generation massively parallel computers requires a team approach that includes materials and chemical scientists, applied mathematicians, and computer scientists.

Applications

This project aims to develop a suite of new theoretical methods in the NWChem computational chemistry software suite  to provide improved capabilities for excited-state dynamics in the gas phase and to add the capability to perform electronically excited-state dynamics in solution.

This projects aims to advance the state of ab initio molecular dynamics simulation in handling hydrated ions in situations relevant to future energy and environmental research applications.

This project aims to develop and implement a suite of new theoretical methods in the NWChem computational chemistry software suite in order to provide improved capabilities for excited-state dynamics in the gas phase and to add the capability to perform electronically excited-state dynamics in solution.

The DGDFT project aims to develop and implement a new Discontinuous Galerkin electronic structure method and to apply it to address fundamental questions on the formation and evolution of the solid-electrolyte interphase layer in lithium-ion cells.

This project aims to develop and implement new methods for optimizing superconductors for energy applications using large-scale computational algorithms and tools.

This project aims to develop and implement new methods and theories to predict electronic excited state phenomena in energy-related materials, such as those used in photovoltaic and photocatalysis applications.

The MEE project develops new and improved models for bound and metastable excited states that incorporate physical science research, applied mathematics advances, and high performance computing tools and techniques.