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Data-flow is a natural approach to parallelism. However, describingdependencies and control between fine-grained data-flow tasks can be complexand present unwanted overheads. TALM (TALM is an Architecture and Language forMulti-threading) introduces a user-defined coarse-grained parallel data-flowmodel, where programmers identify code blocks, called super-instructions, to berun in parallel and connect them in a data-flow graph. TALM has beenimplemented as a hybrid Von Neumann/data-flow execution system: the\emph{Trebuchet}. We have observed that TALM's usefulness largely depends onhow programmers specify and connect super-instructions. Thus, we present\emph{Couillard}, a full compiler that creates, based on an annotatedC-program, a data-flow graph and C-code corresponding to eachsuper-instruction. We show that our toolchain allows one to benefit fromdata-flow execution and explore sophisticated parallel programming techniques,with small effort. To evaluate our system we have executed a set of realapplications on a large multi-core machine. Comparison with popular parallelprogramming methods shows competitive speedups, while providing an easierparallel programing approach.

Couillard: Parallel Programming via Coarse-Grained Data-Flow Compilation