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The data-driven task parallelism execution model can support parallel programming models that are well suited for large-scale distributed-memory parallel computing, for example, simulations and analysis pipelines running on clusters and clouds. We describe a novel compiler intermediate representation and optimizations for this execution model, including adaptions of standard techniques alongside novel techniques. These techniques are applied to Swift/T, a high-level scripting language fordoi:10.1109/ccgrid.2014.115 dblp:conf/ccgrid/ArmstrongWWF14 fatcat:74numjfokbbj5kejpairnteyme