Limitation of branch predictors: a case for multithreaded architectures

Conventional scalar architectures such as the superscalar or multiscalar architectures execute from a single stream, while a multithreaded architecture executes from multiple streams at a time. Several aggressive branch predictors have been proposed with high prediction accuracies but none of them can provide 100% accuracy. We show that multithreaded architecture is a better candidate for utilizing speculative execution than scalar architectures. Generally the branch prediction performance degradation is compounded for larger window sizes on scalar architectures, while for a multithreaded architecture, by increasing the number of executing threads, we could sustain a higher performance for a large aggregated speculative window size. Hence, heavier workloads may increase performance and utilization for multithreaded architectures. We present analytical and simulation results to support our argument.