Backtracking Optimized DDG Directed Scheduling Algorithm for Clustered VLIW Architectures

Abstract

This work presents an instruction schedule approach to improve the performance of clustered VLIW architectures. The proposed scheme is based on a preliminary scheduling phase directed though analyzing of Data Dependence Graph (DDG) and a backtracking optimization scheduling phase bringing further improvement by balancing the workloads through clusters and minimizing the penalties of inter-cluster data communications simultaneously. We have implemented and evaluated the proposed scheme with UTDSP benchmarks. Results show a significant speed-up in performance. The speedup can up to 38.58%, with average speedup ranging from 23.91% (2-Clusters) to up to 26.78% (4-Clusters).