Speculative Updates of Local and Global Branch History: A Quantitative Analysis

In today’s wide-issue processors, even small branch-misprediction rates introduce substantial performance penalties. Worse yet, inadequate branch prediction creates a bottleneck at the fetch stage, restricting other opportunities for improving performance. The choice of how to predict conditional-branch outcomes is the primary lever on prediction accuracy. But the choice of when to update the predictor with branch outcomes is a second powerful lever, and the subject of this paper. In history-based predictors like gshare, many mispredictions result from commit-time update of the history: typical pipelined processors predict branches in the fetch stage, but update the predictor in the commit stage, making the predictor’s state temporarily outof-date. As pipelines grow longer—in particular, when branches can spend many cycles in the instruction window waiting to issue—this problem becomes worse. Prior work on this subject has discussed the need for speculative update in a global-history-based predictor; this paper evaluates speculative history update for both global-history and local-history predictors of various configurations, using cycle-level simulation to show the effects of prediction accuracy and update time on overall performance. The results show the importance of speculative history update, but also that speculative update requires suitable fixup mechanisms for repairing state that has been corrupted by mispredictions. A number of such mechanisms are discussed.