Data Flow Graph Partitioning Algorithms and Their Evaluations for Optimal Spatio-temporal Computation on a Coarse Grain Reconfigurable Architecture

Abstract

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. The task of partitioning is governed by the opposing forces of being able to expose as much parallelism as possible and reducing communication time. We extend Edge-Betweenness Centrality scheme, originally used for detecting community structures in social and biological networks, for partitioning instructions of a dataflow graph. We also implement several other partitioning algorithms from literature and compare the execution time obtained by each of these partitioning algorithms on a CGRA called REDEFINE. Centrality based partitioning scheme outperforms several other schemes with 6-20% execution time speedup for various Cryptographic kernels. REDEFINE using centrality based partitioning performs 9× better than a General Purpose Processor, as opposed to 7.76× better without using centrality based partitioning. Similarly, centrality improves the execution time comparison of AES-128 Decryption from 11× to 13.2×.