A Profile Guided Approach to Optimize Branch Divergence While Transforming Applications for GPUs

Abstract

GPUs offer a powerful bulk synchronous programming model for exploiting data parallelism; however, branch divergence amongst executing warps can lead to serious performance degradation due to execution serialization. We propose a novel profile guided approach to optimize branch divergence while transforming a serial program to a data-parallel program for GPUs. Our approach is based on the observation that branches inside some data parallel loops although divergent, exhibit repetitive regular patterns of outcomes. By exploiting such patterns, loop iterations can be aligned so that the corresponding iterations traverse the same branch path. These aligned iterations when executed as a warp in a GPU, become convergent. We propose a new metric based on the repetitive pattern characteristics that indicates whether a data-parallel loop is worth restructuring. When tested our approach on the well-known Rodinia benchmark, we found that it is possible to achieve upto 48% performance improvement by loop restructuring suggested by the patterns and our metrics.