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, 2007, Elsevier.
  • M.A. Jessee, Cross-Section Adjustment Techniques for BWR Adaptive Simulation , PhD thesis, 2008.


  • 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, 2008.
  • R. Younis and K. Aziz, Parallel Automatically Differentiable Data-Types for Next-Generation Simulator Development , SPE Reservoir Simulation Symposium, 2007.
  • 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-.
  • L. Kaufman, Calculating Dispersion Derivatives in Fiber-Optic Design , Journal of Lightwave Technology, Vol 25, No 3, pg 811-819, 2007, OSA.
  • S. Khatiwala, Ocean Modelling , Ocean Modelling, Vol 23, pg 121-129, 2008.
  • 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, 2008, Springer.
  • K. Robenack, 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, 2007, Citeseer.
  • 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, 2009, Elsevier.