论文信息 - Dataflow Graph Partitioning for Optimal Spatio-Temporal Computation on a Coarse Grain Reconfigurable Architecture

Dataflow Graph Partitioning for Optimal Spatio-Temporal Computation on a Coarse Grain Reconfigurable Architecture

Coarse Grain Reconfigurable Architectures(CGRA) support Spatial and Temporal computation to speedup execution and reduce reconfiguration time. Thus compilation involves partitioning instructions spatially and scheduling them temporally. We extend Edge-Betweenness Centrality scheme, originally used for detecting community structures in social and biological networks, for partitioning instructions of a dataflow graph. Comparisons of execution time for several applications run on a simulator for REDEFINE, a CGRA proposed in literature, indicate that Centrality scheme outperforms several other schemes with 2-18% execution time speedup.

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