DocumentCode :
1260870
Title :
Reducing memory latency via read-after-read memory dependence prediction
Author :
Moshovos, Andreas ; Sohi, Gurindar S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Toronto Univ., Ont., Canada
Volume :
51
Issue :
3
fYear :
2002
fDate :
3/1/2002 12:00:00 AM
Firstpage :
313
Lastpage :
326
Abstract :
We observe that typical programs exhibit highly regular read-after-read (RAR) memory dependence streams. To exploit this regularity, we introduce read-after-read memory dependence prediction. This technique predicts whether: 1) a load will access a memory location that a preceding load accesses and 2) exactly which this preceding load is. This prediction is done without actual knowledge of the corresponding memory addresses. We also present two techniques that utilize RAR memory dependence prediction to reduce memory latency. In the first technique, a load may obtain a value by naming a preceding load with which an RAR dependence is predicted. The second technique speculatively converts a series of LOAD1-USE1,...,LOADN-USEN chains into a single LOAD1-USE1...USEN producer/consumer graph. Our techniques can be implemented as small extensions to the previously proposed read-after-write (RAW) dependence prediction-based speculative memory cloaking and speculative memory bypassing. Performance experimentation results of RAR-based techniques are provided
Keywords :
computer architecture; memory architecture; microprocessor chips; resource allocation; storage allocation; RAR dependences; RAR memory dependence prediction; dynamically scheduled superscalar processor; memory addresses; memory latency; memory latency reduction; memory location; preceding load; producer/consumer graph; read-after-read memory dependence prediction; read-after-write dependence prediction-based speculative memory cloaking; regular read-after-read memory dependence streams; speculative memory bypassing; typical programs; Delay;
fLanguage :
English
Journal_Title :
Computers, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9340
Type :
jour
DOI :
10.1109/12.990129
Filename :
990129
Link To Document :
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