Compiler and Runtime Support for Structured and Block Structured Applications

Scientific and engineering applications often involve structured meshes. These meshes may be nested (for multigrid or adaptive codes) and/or irregularly coupled (called Irregularly Coupled Regular Meshes). We have designed and implemented a runtime library for parallelizing this general class of applications on distributed memory parallel machines in an efficient and machine independent manner. In this paper we present how this runtime library can be integrated with compilers for High Performance Fortran (HPF) style parallel programming languages. We discuss how we have integrated this runtime library with the Fortran 90D compiler being developed at Syracuse University and provide experimental data on a block structured NavierStokes solver template and a small multigrid example parallelized using this compiler and run on an Intel iPSC/860. We show that the compiler parallelized code performs within 20% of the code parallelized by inserting calls to the runtime library manually. 1 Intr..

Hybrid analysis of memory references and its application to automatic parallelization

Executing sequential code in parallel on a multithreaded machine has been an elusive goal of the academic and industrial research communities for many years. It has recently become more important due to the widespread introduction of multicores in PCs. Automatic multithreading has not been achieved because classic, static compiler analysis was not powerful enough and program behavior was found to be, in many cases, input dependent. Speculative thread level parallelization was a welcome avenue for advancing parallelization coverage but its performance was not always optimal due to the sometimes unnecessary overhead of checking every dynamic memory reference. In this dissertation we introduce a novel analysis technique, Hybrid Analysis, which unifies static and dynamic memory reference techniques into a seamless compiler framework which extracts almost maximum available parallelism from scientific codes and incurs close to the minimum necessary run time overhead. We present how to extract maximum information from the quantities that could not be sufficiently analyzed through static compiler methods, and how to generate sufficient conditions which, when evaluated dynamically, can validate optimizations. Our techniques have been fully implemented in the Polaris compiler and resulted in whole program speedups on a large number of industry standard benchmark applications