This project will focus on the development and evaluation of scheduling and run-time optimization techniques for supporting the parallelization of irregular scientific code with mixed granularity on distributed memory machines and workstation clusters. The main research topics will be to develop general space/time efficient scheduling techniques and integrate memory and communication support for solving large-scale irregular problems. A software tool will be developed for evaluating the effectiveness of the proposed techniques and providing an infrastructure which can be used by other researchers. Interoperability with other tools will also be investigated if time and funds permit. The targeted applications will be mainly in irregular scientific computing such as iterative methods for nonlinear equations and sparse matrix based solvers. The educational activities will include course development for undergraduate parallel computing, with an emphasis on parallel programming and basic scientific algorithms on distributed and shared memory machines. ***
