Existing restructuring compilers have been successful in detecting the parallelism of regular programs, i.e., codes with relatively simple access patterns that are statically (compile time) defined, by using current dependence tests and symbolic methods. Unfortunately, for more than 50% of programs the access pattern are either very complex or are completely undefined before execution. Examples of such "cycle consuming" applications are SPICE, DYNA-3D, GAUSSIAN, DMOL, CHARMM and FIDAP. As modeling techniques become more sophisticated, future large scale simulations will be mostly dynamic in nature, thus increasing the fraction of statically non-analyzable codes and therefore effectively decreasing the coverage of current compiler technology. Sparse, dynamic programs are fundamentally intractable through classic compile-time analysis techniques making parallelization or other optimizations impossible. This work will extend the coverage of current technology to dynamic programs by creating a framework that will complement static compilation with run--time analysis and optimization techniques. Application level programmable hardware for the acceleration of certain simple, repetitive operations will also be studied. Finally, an adaptive strategy in which static and run-time methods will be connected in an adaptive loop with hardware and software performance monitors and statistical databases will tune the application to its data during execution.
