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BMDFM: a hybrid dataflow runtime parallelization environment for shared memory multiprocessors

To complement existing compiler-optimization methods we propose a programming model and a runtime system called BMDFM (Binary Modular DataFlow Machine), a novel hybrid parallel environment for SMP (Shared Memory Symmetric Multiprocessors), that creates a data-dependence graph and exploits parallelism of user application programs at run time. This thesis describes the design and provides a detailed analysis of BMDFM, which uses a dataflow runtime engine instead of a plain fork-join runtime library, thus providing transparent dataflow semantics on the top virtual machine level. Our hybrid approach eliminates disadvantages of the parallelization at compile-time, the directive based paradigm and the dataflow computational model. BMDFM is portable and is already implemented on a set of available SMP platforms. The transparent dataflow paradigm does not require parallelization and synchronization directives. The BMDFM runtime system shields the end-users from these details.