Improving speculative loop parallelization via selective squash and speculation reuse

Abstract

Speculative parallelization is a powerful technique to parallelize loops with irregular data dependencies. In this poster, we present a value-based selective squash protocol and an optimistic speculation reuse technique that leverages an extended notion of silent stores. These optimizations focus on reducing the number of squashes due to dependency violations. Our proposed optimizations, when applied to loops selected from standard benchmark suites, demonstrate an average (geometric mean) 2.5x performance improvement. This improvement is attributed to a 94% success in speculation reuse and a 77% reduction in the number of squashed threads compared to an implementation that, in such cases of squashes, would have squashed all the successors starting from the oldest offending one.