NDLI: Dynamic data dependence tracking and its application to branch prediction

Content Provider	IEEE Xplore Digital Library
Author	Lei Chen Dropsho, S. Albonesi, D.H.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Rochester Univ., NY, USA (Lei Chen)
Abstract	To continue to improve processor performance, microarchitects seek to increase the effective instruction level parallelism (ILP) that can be exploited in applications. A fundamental limit to improving ILP is data dependences among instructions. If data dependence information is available at run-time, there are many uses to improve ILP. Prior published examples include decoupled branch execution architectures and critical instruction detection. In this paper, we describe an efficient hardware mechanism to dynamically track the data dependence chains of the instructions in the pipeline. This information is available on a cycle-by-cycle basis to the microengine for optimizing its performance. We then use this design in a new value-based branch prediction design using available register value information (ARVI). From the use of data dependence information, the ARVI branch predictor has better prediction accuracy over a comparably sized hybrid branch predictor With ARVI used as the second-level branch predictor the improved prediction accuracy results in a 12.6% performance improvement on average across the SPEC95 integer benchmark suite.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Comput. Architecture
Starting Page	65
Ending Page	76
File Size	365273
Page Count	12
File Format	PDF
ISBN	0769518710
ISSN	15300897
DOI	10.1109/HPCA.2003.1183525
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-02-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Registers Microprocessors Application software Parallel processing Runtime Accuracy Out of order Read-write memory Computer architecture Computer science
Content Type	Text
Resource Type	Article

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