Convergence and Scalarization in Whole Function Vectorization
Feng Yue, Jianmin Pang, Jiuzhen Jin, Dai Chao · 2013
When implementing SPMD programs on multi core platforms, whole function vectorization is an important optimization method. SPMD program has drawback that lots of instructions across multi threads are redundant which is sustained in vectorization. This paper proposes to alleviate this overhead by detecting scalar operations and extract them out in vectorization instructions. An algorithm is designed to deal with control flow and data flow synchronously in which convergent and invariance analysis is employed to statically identify convergent execution and invariant values or instructions. Our algorithm is effectively on implementing SPMD programs on multi core platforms. The experiments show our method could improve the execution efficiency by 13.3%.