The Institute for Combinatorial Scientific Computing and Petascale Simulations (CSCAPES, pronounced "seascapes") is one of the four Institutes established nationwide in September 2006 as the new component of the second cycle of the DOE initiative Scientific Discovery through Advanced Computing (SciDAC). The CSCAPES Institute has a two-fold purpose:

• To accelerate the development and deployment of fundamental enabling technologies in high performance computing, by creating algorithms and software tools for key combinatorial problems in scientific computing at the petascale, and
• To foster the next generation of researchers capable of effectively applying combinatorial techniques to scientific computing, by training graduate students and post-doctoral associates, conducting outreach workshops and tutorials, and publishing in the scientific literature.

The focus areas of CSCAPES are load balancing in parallel computation, automatic differentiation, and advanced methods for sparse matrix computations. To realize its goals, CSCAPES is committed to working closely with other SciDAC Institutes, Centers for Enabling Technologies, and Science Application Partnerships, as well as with relevant bodies in academia and industry, internationally. The CSCAPES Institute is a collaborative effort among investigators from Purdue University, Old Dominion University, Sandia National Laboratories, Argonne National Laboratory, Ohio State University, and Colorado State University. The Institute is funded by DOE's Office of Science.

CSCAPES Homepage

Zoltan is a toolkit for data management in parallel (distributed) computing. It is the leading tool for partitioning and dynamic load balancing for scientific computing. We have recently developed a fully parallel hypergraph partitioner. Zoltan is open-source software and available for download, either separately or as part of Trilinos. Isorropia is a Trilinos package for sparse matrix partitioning and provides a matrix (Epetra) based interface to Zoltan. We have developed novel 2D partitioning methods which will be deployed in Isorropia.

The EASI is a math/CS research institute funded by Office of Science in 2009. Participants include Sandia, Oak Ridge, UC Berkeley, and UIUC. The goal is to improve performance for scientific computing on multi-core and emerging architectures, with focus on future extreme-scale computers.

We will model cracks and fracture in ice sheets and their impact on global climate. Computational tools include XFEM, multigrid solvers, and load balancing for parallel computing. This project is led by Columbia University, and part of ISICLES.

Support preconditioners is an emerging class of preconditioners for symmetric linear systems with strong theoretical properties. I have made important contributions in this area, see my list of publications. Learn more about support preconditioners at www.preconditioners.com.

Sandia has in collaboration with the EPA developed an early warning system for detecting contamination of drinking water. This is part of the TEVA (Threat Ensemble Vulnerability Assessment) and SPOT (Sensor Placement Optimization Toolkit) projects. (Press release.) My contribution has been in optimization methods for sensor placement, in particular memory-efficient Lagrangian methods. The EPA/Sandia/Argonne team was a finalist for the prestigous 2008 Edelman award in operations research. (Description of the finalists.)

Xyce is a parallel circuit simulator, similar to SPICE. A key problem is to solve highly ill-conditioned sparse linear systems of equations. Traditionally, direct solvers are used, but these do not scale well in parallel. Most iterative solvers (preconditioners) do not work well. I work on both direct and iterative methods that are suitable for parallel computing.