Dynamic issue queue capping for simultaneous multithreaded processors

A simultaneous multithreaded (SMT) processor mixes multiple instruction streams in its superscalar out-of-order execution core for higher throughput. To achieve this, a superscalar processor is modified in such a way that some of its resources are duplicated and the rest is shared among multiple threads. The issue queue (IQ), which holds all waiting instructions until they become ready and scheduled for execution, is among these shared resources. A baseline unmanaged IQ can give an unexpectedly low performance since a hungry thread can tie up most of the IQ entries. This type of scenario is also worse in terms of the fairness metric since some of the threads may experience starvation. Earlier studies propose both static and a limited type of dynamic capping of the IQ entries for regulating IQ traffic and providing better SMT throughput and fairness. In this study, we propose an efficiency-based dynamic capping (EDC) algorithm that calculates an efficiency metric for each thread and allocates the IQ entries for maximizing the throughput and the fairness metrics. EDC gives 3.6% better throughput and 3.9% better fairness results compared to the current state-of-the-art algorithms, on the average.