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Author Desikan, Rajagopalan ♦ Burger, Doug ♦ Keckler, Stephen W. ♦ Sethumadhavan, Simha
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Selective re-execution ♦ Edge architectures ♦ Load-store dependence prediction ♦ Selective replay ♦ Mis-speculation recovery ♦ Speculative dataflow machines
Abstract Pipeline flushes are becoming increasingly expensive in modern microprocessors with large instruction windows and deep pipelines. Selective re-execution is a technique that can reduce the penalty of mis-speculations by re-executing only instructions affected by the mis-speculation, instead of all instructions. In this paper we introduce a new selective re-execution mechanism that exploits the properties of a dataflow-like Explicit Data Graph Execution (EDGE) architecture to support efficient mis-speculation recovery, while scaling to window sizes of thousands of instructions with high performance. This distributed selective re-execution (DSRE) protocol permits multiple speculative waves of computation to be traversing a dataflow graph simultaneously, with a commit wave propagating behind them to ensure correct execution. We evaluate one application of this protocol to provide efficient recovery for load-store dependence speculation. Unlike traditional dataflow architectures which resorted to single-assignment memory semantics, the DSRE protocol combines dataflow execution with speculation to enable high performance and conventional sequential memory semantics. Our experiments show that the DSRE protocol results in an average 17% speedup over the best dependence predictor proposed to date, and obtains 82% of the performance possible with a perfect oracle directing the issue of loads.
Description Affiliation: The University of Texas at Austin (Desikan, Rajagopalan; Sethumadhavan, Simha; Burger, Doug; Keckler, Stephen W.)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1983-05-01
Publisher Place New York
Journal ACM SIGPLAN Notices (SIGP)
Volume Number 39
Issue Number 11
Page Count 13
Starting Page 120
Ending Page 132


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Source: ACM Digital Library