When Merging and Branch Predictors Collide

Merging is a building block for many computational domains. In this work we consider the relationship between merging, branch predictors, and input data dependency. Branch predictors are ubiquitous in modern processors as they are useful for many high performance computing applications. While it is well known that the performance and the branch prediction accuracy go hand-in-hand, these have not been studied in the context of merging. We thoroughly test merging using multiple input array sizes and values using the same code and compile optimizations. As the number of possible keys increase, so the do the number of branch mis-predictions - resulting in reduced performance. The reduction in performance can be as much as
$5X$
. We explain this phenomenon using a visualization technique called Merge Path that intuitively shows this. We support this visualization approach with modeling, thorough testing, and analysis on multiple systems.