We list below examples of computational science and engineering applications
that have been enabled by some CSCAPES developed technology.
A new CSCAPES-developed hypergraph model has been implemented and tested by
ITAPS collaborators at RPI (Min Zhou, Mark Shephard). Preliminary results
show a reduction in both memory and communication compared to the traditional graph model.
Simulated Moving Bed process.
SMB is a model for a purification technique widely used in the
chemical, food, and pharmaceutical industry to separate liquid
chemicals that are thermally unstable or have high boiling points.
CSCAPES developed coloring techniques reduced the time and
space needed to compute Jacobians in an SMB process by several
orders of magnitude. Read this paper for details
Electric power flow optimization.
The optimization of electric power flow in a network consisting of observable
and unobservable parts can be formulated as an unconstrained optimization
problem. Solving the optimization problem using an interior point method relies
on the provision of the Hessian of a Lagrange function.
CSCAPES developed star and acyclic coloring techniques have made it possible
to reduce the runtime requirement of the Hessian computation by several
orders of magnitude.
Read this paper for details
Fluid Flow Computations for Surgical Simulations
Researchers at Rensselaer Polytechnic Institute have demonstrated
strong scalability of over 90% in unstructured-mesh fluid flow
simulations on 100K+ processor cores. They use the graph and hypergraph
partitioning tools in the Zoltan dynamic load balancing library to
distributed meshes (up to 1B elements) across the cores. The target
application for their software is simulation of surgical options to fix
abdominal aortic aneurysms. In these simulations, short time-to-solution
(and, thus, excellent strong scalability) is absolutely required. For this
work, the Rensselaer team was recognized as a finalist for the 2009 Gordon
Bell Prize. Read their paper for details.
Design and Optimization of Large Accelerator Systems through High-fidelity Electromagnetic Simulations
Researchers at Stanford Linear Accellerator (SLAC) have developed the parallel 3D Finite Element electromagnetic particle-in-cell code, Pic3P. Designed for simulations of beam-cavity interactions dominated by space charge effects, Pic3P solves the complete set of Maxwell-Lorentz equations. Higher-order Finite Element Methods with adaptive refinement on unstructured meshes lead to highly efficient use of computational resources. Massively parallel processing with dynamic load balancing (using the Zoltan toolkit) enables modeling with unprecedented accuracy. Pic3P has been run on up to 24K cores with 750 million degrees of freedom and up to 5 billion particles.
SciDAC08 paper, Pic3P paper
And here is a list of examples of publications that cite a
CSCAPES-developed software tool.
C. Wunsch, and P. Heimbach,
Practical global oceanic state estimation
Physica D: Nonlinear Phenomena, Vol 230, No 1-2, pg 197-208,
Cross-Section Adjustment Techniques for BWR Adaptive Simulation
M.Y. Chu and H.M.Chen,
Mutual Information Based Non-rigid Image Registration
Using Adaptive Grid Generation:
GPU Implementation And Application To Breast MRI
Computer Science Engineering,
R. Younis and K. Aziz,
Parallel Automatically Differentiable Data-Types
for Next-Generation Simulator Development
SPE Reservoir Simulation Symposium,
R.D. Kirkman and M. Metzger,
Sensitivity analysis of low Reynolds number channel flow
using the finite volume method
International Journal for Numerical Methods in Fluids, Vol 57, No 8, pg 1023,
2008, Chichester; New York: Wiley, c1981-.
Calculating Dispersion Derivatives in Fiber-Optic Design
Journal of Lightwave Technology, Vol 25, No 3, pg 811-819,
Ocean Modelling, Vol 23, pg 121-129,
D.I. Papadimitriou and K.C. Giannakoglou,
Aerodynamic Shape Optimization
Using First and Second Order Adjoint and Direct Approaches
Archives of Computational Methods in Engineering, Vol 15, No 4, pg 447-488,
Controller design for nonlinear multi-input-multi-output systems
based on an algorithmic plant description
Mathematical and Computer Modelling of Dynamical Systems, Vol 13, No 2, pg 193-209,
2007, Taylor and Francis Ltd.
J.S. Baras, V. Tabatabaee, G. Papageorgiou, N. Rentz, and Y. Shang,
Loss Model Approximations and Sensitivity Computations
for Wireless Network Design
IEEE Military Communications Conference, 2007. MILCOM 2007, pg 1-7,
A. Rasch, H.M. Bucker, and Bardow, A.,
Software supporting optimal experimental design:
A case study of binary diffusion using EFCOSS
Computers and Chemical Engineering, Vol 33, No 4, pg 838-849,