Architectural Support for Scalable Speculative Parallelization in Shared -Memory Multiprocessors

108 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001.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.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD