Alloyed Global and Local Branch History: A Robust Solution to Wrong-History Misprediction

Report ID: TR-606-99
Author: Martonosi, Margaret / Skadron, Kevin / Clark, Douglas W.
Date: 1999-10-00
Pages: 26
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Abstract:

The need for accurate conditional-branch prediction is well known: mispredictions waste large numbers of cycles, inhibit out-of-order execution, and waste power on mis-speculated computation. Prior work on branch-predictor organization has focused mainly on how to reduce conflicts in the branch-predictor structures, while relatively little work has explored other causes of mispredictions. Some prior work has identified other categories of mispredictions, but this paper organizes these categories into a broad taxonomy of misprediction types. Using the taxonomy, this paper goes on to show that other categories---especially wrong-history mispredictions---are often more important than conflicts. This is true even if just a very simple conflict-reduction technique is used.

Wrong-history mispredictions arise because current two-level, history-based predictors provide only global or only local history. Their contribution to the overall misprediction rate is substantial because most programs have some branches that require global history and also some that require local history. If only one or the other type of history is available, many branches are therefore penalized. For SPECint95 programs using a global-history predictor, wrong-history mispredictions account for 35--50% of the total misprediction rate. By comparison, conflicts only account for 15-20%. Hybrid predictors are one proposed solution; they use both a global-history and a local-history component. Unfortunately, hybrid predictors only work well with large hardware budgets, because they must subdivide the available area into subcomponents.

Based on these observations, this paper proposes alloying local and global history together in a two-level branch predictor structure. This is a simple but superior technique for making global and local history simultaneously available and eliminating wrong-history mispredictions. Unlike hybrid prediction, alloying gives robust performance for branch-predictor hardware budgets ranging from very large to very small. Alloying also consistently outperforms other two-level organizations. In fact, a small alloyed predictor often performs as well as a much larger global-history predictor.