Please use this identifier to cite or link to this item:
Title: Cyclic dependence based data reference prediction
Authors: Chi, Chi-Hung 
Cheung, Chin-Ming
Yuan, Jun-Li 
Issue Date: 1999
Citation: Chi, Chi-Hung,Cheung, Chin-Ming,Yuan, Jun-Li (1999). Cyclic dependence based data reference prediction. Proceedings of the International Conference on Supercomputing : 127-134. ScholarBank@NUS Repository.
Abstract: Current work in data prediction and prefetching is mainly focused on one individual predictor per each class of data references. Despite the increasing complexity of these hybrid predictors, their prefetch coverage is still very limited. To reduce the complexity of the predictors and to expand the coverage of data prediction, we propose a novel data predictor, called the Cyclic Dependence based data Predictor (CDP), in this paper. Based on the runtime analysis for value dependence, registers used in the address calculation of a memory access instruction in a loop are classified into cyclic dependent registers and acyclic dependent registers. The complexity and predictability of data references will be determined by the path length of the cycle that contains the index pointer registers. To further illustrate its importance, we generalize our previously proposed Reference Value Prediction Cache with this CDP predictor. Simulation shows that significant reduction in memory latency can be obtained, especially for those using complex data pointer structures.
Source Title: Proceedings of the International Conference on Supercomputing
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Sep 9, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.