Detecting global stride locality in value streams

• Authors: Huiyang Zhou;Jill Flanagan;Thomas M. Conte
• Venue: Proceedings of the 30th annual international symposium on Computer architecture
• Year: 2003

Value prediction exploits localities in value streams. Previous research focused on exploiting two types of value localities, computational and context-based, in the local value history, which is the value sequence produced by the same instruction that is being predicted. Besides the local value history, value locality also exists in the global value history, which is the value sequence produced by all dynamic instructions according to their execution order. In this paper, a new type value locality, the computational locality in the global value history is studied. A novel prediction scheme, called the gDiff predictor, is designed to exploit one special and most common case of this computational model, the stridebased computation, in the global value history. Such a scheme provides a general framework to exploit global stride locality in any value stream. Experiments show that there exists very strong stride type of locality in global value sequences. Ideally, the gDiff predictor can achieve 73% prediction accuracy for all value producing instructions without any hybrid scheme, much higher than local stride and local context prediction schemes. However, the capability of realistically exploiting locality in global value history is greatly challenged by the value delay issue, i.e., the correlated value may not be available when the prediction is being made. We study the value delay issue in an out-of-order (OOO) execution pipeline model and propose a new hybrid scheme to maximize the exploitation of the global stride locality. This new hybrid scheme shows 91% prediction accuracy and 64% coverage for all value producing instructions. We also show that the global stride locality detected by gDiff in load address streams provides strong capabilities in predicting load addresses (coverage 63% and accuracy 86%) and in predicting addresses of missing loads (33% coverage and 53% accuracy).