In this work, we present a novel approach that integrates Data Dependence Graph (DDG) and Scalar Expansion techniques into LLVM’s LoopDistribute pass to enhance auto-vectorization optimization. We identify key limitations in the current LoopDistribution pass and devise a DDG-based partitioning algorithm with scalar expansion technique to enable more precise loop distribution and better vectorization opportunities. The talks will cover both the algorithm for the proposed method and a compile-time regression report for our initial implementation on benchmarks.