Improving dynamic prediction accuracy through multi-level phase analysis

Phase analysis, which classifies the set of execution intervals with similar execution behavior and resource requirements, has been widely used in a variety of dynamic systems, including dynamic cache reconfiguration, prefetching and race detection. While phase granularity has been a major factor to the accuracy of phase prediction, it has not been well investigated yet and most dynamic systems usually adopt a fine-grained prediction scheme. However, such a scheme can only take account of recent local phase information and could be frequently interfered by temporary noises due to instant phase changes, which might notably limit the prediction accuracy.

In this paper, we make the first investigation on the potential of multi-level phase analysis (MLPA), where different granularity phase analysis are combined together to improve the overall accuracy. The key observation is that a coarse-grained interval, which usually consists of stably-distributed fine-grained intervals, can be accurately identified based on the fine-grained intervals at the beginning of its execution. Based on the observation, we design and implement a MLPA scheme. In such a scheme, a coarse-grained phase is first identified based on the fine-grained intervals at the beginning of its execution. The following fine-grained phases in it are then predicted based on the sequence of fine-grained phases in the coarse-grained phase. Experimental results show such a scheme can notably improve the prediction accuracy. Using Markov fine-grained phase predictor as the baseline, MLPA can improve prediction accuracy by 20%, 39% and 29% for next phase, phase change and phase length prediction for SPEC2000 accordingly, yet incur only about 2% time overhead and 40% space overhead (about 360 bytes in total).

To demonstrate the effectiveness of MLPA, we apply it to a dynamic cache reconfiguration system which dynamically adjusts the cache size to reduce the power consumption and access time of data cache. Experimental results show that MLPA can further reduce the average cache size by 15% compared to the fine-grained scheme.