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Distributed, dynamic data flow is an execution model well-suited for many large-scale parallel applications, particularly scientific simulations and analysis pipelines running on large, distributed-memory clusters. In this paper we describe compiler optimization techniques and an intermediate representation for distributed dynamic data flow programs. These techniques are applied to Swift/T, a high-level declarative language that allows flexible data flow composition of functions written in other programming languages such a C or Fortran. We show that compiler optimization can reduce communication overhead by 70-93% on distributed memory systems, making the high-level language competitive with hand-coded coordination logic for certain common application styles
Christian PerezGille Fedak
On Component Models to Deploy Application on Clouds
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