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

TL;DR

Couillard is a compiler that simplifies parallel programming by translating annotated C code into a data-flow graph and super-instruction code, enabling efficient coarse-grained parallelism on multi-core systems.

Contribution

It introduces Couillard, a compiler that automates data-flow graph creation from annotated C programs, facilitating easier and effective parallel programming.

Findings

Achieved competitive speedups on real applications.

Enabled easier parallel programming with small effort.

Demonstrated effectiveness on large multi-core machines.

Abstract

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