• Parallelizing Irregular Applications
• Lonestar Benchmark Suite

• Compiling for Distributed Memory Computers
• Control Dependence Computation
• Empirical Optimization
• Fault Tolerance
• Fractal Symbolic Analysis
• Functional Languages with Logic Variables
• Memory Hierarchy Optimizations
• Model Checking for Hierarchical State Machines
• Program Representation
• Static Single Assignment Form
• Synthesis of Irregular Codes
• Web Services for Distributed Scientific Computing

The goal of our research is to build intelligent software systems for automatically enhancing the efficiency and survivability of programs, thereby enabling applications programmers to focus on high-level algorithmic issues. We work in several areas, ranging from restructuring and optimizing compilers to protocols for fault-tolerant computing systems.

Our group is best known for its contributions to automatic parallelism and locality enhancement of programs. These advances are used in software products from Intel, IBM, Hewlett-Packard, Silicon Graphics and Digital Equipment Corporation, among other companies.

We are currently working in the following areas:

• Self-optimizing high-performance library generation: We want to understand how model-driven optimization and AI-based search techniques can be combined in systems for producing self-tuned high-performance libraries.
• Automatic application-level fault tolerance: We are building a pre-compiler and a runtime coordination layer for implementing non-blocking, coordinated, application-level check-pointing of MPI/OpenMP programs.
• Web-services for grid-computing: We are implementing a coupled thermal/mechanical/fluid simulation system that uses web-services based on SOAP and XML for communication and co-ordination.