Architectural Support for Scalable Speculative Parallelization in Shared -Memory Multiprocessors

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Cintra, Marcelo Hehl

Contributors dc:contributor

• Torrellas, Josep

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3017054

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/80706