With trend toward using GPUs for a diverse range of applications (e.g. vision and scientific computing) new challenges have been raised. One of the main challenges is inter-warp conflicts in the shared resources including I$, D$ and compute unites. The conflicts in the shared resources mainly caused by inter-warp divergence which is uneven execution progress across the concurrent warps.  Excessive inter-warp divergence may hinder GPUs to achieve their peak throughput. This motivates the need for approaches that manage inter-warp divergence, avoiding I$ conflicts, for divergence-sensitive benchmarks.