ملخص: The key to high performance in Simultaneous MultiThreaded (SMT) processors lies in optimizing the distribution of shared resources to active threads. Existing resource distribution techniques optimize performance only indirectly. They infer potential performance bottlenecks by observing indicators, like instruction occupancy or cache miss counts, and take actions to try to alleviate them. While the corrective actions are designed to improve performance, their actual performance impact is not known since end performance is never monitored. Consequently, potential performance gains are lost whenever the corrective actions do not effectively address the actual bottlenecks occurring in the pipeline. We propose a different approach to SMT resource distribution that optimizes end performance directly. Our approach observes the impact that resource distribution decisions have on performance at runtime, and feeds this information back to the resource distribution mechanisms to improve future decisions. By evaluating many different resource distributions, our approach tries to learn the best distribution over time. Because we perform learning online, learning time is crucial. We develop a hill-climbing algorithm that quickly learns the best distribution of resources by following the performance gradient within the resource distribution space.We also develop several ideal learning algorithms to enable deeper insights through limit studies.

لغة: إنجليزية