Recovery Methodology to Avoid Loss for SLP

Nowadays more and more processors are integrated with SIMD extensions, and many compilers have applied auto-vectorization. SLP is an vectorization algorithm that could vectorize scientific applications more effectively than traditional algorithm. However, if basic blocks have not vectorized efficiently by SLP then the vectorization performance will degrade. To solve that problem this paper brings SLP that applied recovery methodology. The algorithm adopts SLP algorithm to vectorize program and then esitimate the vectorization benifit based cost model, at last recover the basic blocks that haven’t vectorized efficiently to their original states. Experiment results indicate that with the adoption of the new policy, the speedup gain for some applications can reach 29.4%.