Architectural Support for Scalable Speculative Parallelization in Shared -Memory Multiprocessors
Cintra, Marcelo Hehl
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https://hdl.handle.net/2142/80706
Description
Title
Architectural Support for Scalable Speculative Parallelization in Shared -Memory Multiprocessors
Author(s)
Cintra, Marcelo Hehl
Issue Date
2001
Doctoral Committee Chair(s)
Torrellas, Josep
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
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.
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