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Title:Architectural Support for Scalable Speculative Parallelization in Shared -Memory Multiprocessors
Author(s):Cintra, Marcelo Hehl
Doctoral Committee Chair(s):Torrellas, Josep
Department / Program:Electrical Engineering
Discipline:Electrical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer Science
Abstract:In this thesis, we also propose a new approach to reduce the cost of handling cross-thread data dependence violations: run-time learning. Using a new module called the Violation Prediction Table, the hardware learns to stall a thread when it seems likely to trigger a squash, and to release it when it is unlikely to trigger one. Simulations of a 16-processor scalable system show that the scheme is very effective. For a protocol that keeps speculation state on a per-line basis at the system level, learning eliminates on average 84% of the squashes. The resulting system runs on average 43% faster, and its performance is very close to a system with perfect prediction.
Issue Date:2001
Description:108 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001.
Other Identifier(s):(MiAaPQ)AAI3017054
Date Available in IDEALS:2015-09-25
Date Deposited:2001

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