基于指令距离的存储相关性预测方法

存储相关性预测对于减少存储相关性冲突、提高微处理器性能具有十分重要的作用。针对传统相关性预测器硬件开销大、可实现性较差的缺点，通过对存储相关性的局部性分析，提出了一种基于指令距离的存储相关性预测方法。该方法充分利用了发生存储相关性冲突的指令在指令距离上的局部性，预测冲突指令的指令距离，进而控制部分访存指令的发射时机，大大减少了存储相关性冲突的次数。实验结果表明，在硬件开销约为1KB的情况下，使用基于指令距离的相关性预测器后，每个时钟周期平均执行的指令数可以提高1.70%，最高可以提高5.11%。在硬件开销较小的情况下，较大程度提高了微处理器的性能。

Memory dependence prediction method based on instruction distance

Memory dependence prediction plays a very important role to reduce memory order violation and improve microprocessor performance. However, the traditional methods usually have large hardware overhead and poor realizability. Through the analysis of memory dependence's locality, this paper proposed a new memory predictor based on instruction distance. Compared to other memory dependence predictors, this predictor made full use of memory dependence's locality on instruction distance, predicted memory instruction' violation distance, controlled the speculation of a few instructions, finally deduced the number of memory order violation and improved the performance. The simulation results show that with only 1KB hardware budget, average Instruction Per Cycle (IPC) get a 1.70% speedup, and the most improvement is 5.11%. In the case of a small hardware overhead, the performance is greatly improved.