In this paper, we propose a machine learning algorithm to control instruction fetch bandwidth in a simultaneous multithreaded CPU. In a simultaneous multithreaded CPU, multiple threads occupy pools of hardware resources in the same clock cycle. Under some conditions, one or more threads may undergo a period of inefficiency, e.g., a cache miss, thereby inefficiently using shared resources and degrading the performance of other threads. If these inefficiencies can be identified at runtime, the offending thread can be temporarily blocked from fetching new instructions into the pipeline and given time to recover from its inefficiency, and prevent the shared system resources from being wasted on a stalled thread. In this paper, we propose a machine learning approach to determine when a thread should be blocked from fetching new instructions. The model is trained offline and the parameters embedded in a CPU, which can be queried with runtime statistics to determine if a thread is running inefficiently and should be temporarily blocked from fetching. We propose two models: a simple linear model and a higher-capacity neural network. We test each model in a simulation environment and show that system performance can increase by up to 19% on average with a feasible implementation of the proposed algorithm.
Efficient local locking for massively multithreaded in-memory hash-based operators