Real-world graph applications are generally larger than the size of the cache itself. Due to this reason, the memory hierarchy was identified as a key bottleneck by the earlier works. Undoubtedly, the performance can be achieved by improving cache, there is still a scope for performance gain by improving branch prediction accuracy. In graph processing applications, the occurrence of branch mispredictions is very frequent and is a major limitation for the overall performance. Within a program, there are different kinds of branches that recur throughout its execution. Although lots of branch predictors (BP) have been developed earlier to capture the static and dynamic behavior of branches. Branch predictors can yet be further optimized to handle the branches that cause mispredictions.

翻译：真实世界的图应用通常规模远超缓存本身容量。因此，先前的研究将内存层次结构确定为主要瓶颈。尽管通过改进缓存无疑能提升性能，但通过提高分支预测准确性仍有性能提升空间。在图处理应用中，分支误预测频繁发生，成为整体性能的主要限制因素。程序内部存在多种类型的分支，它们在整个执行过程中反复出现。尽管已有大量分支预测器被研发出来以捕获分支的静态与动态行为，但分支预测器仍可进一步优化，以处理导致误预测的分支。