Modeling optimistic concurrency using quantitative dependence analysis

Abstract

This work presents a quantitative approach to analyze parallelization opportunities in programs with irregular memory access where potential data dependencies mask available parallelism. The model captures data and causal dependencies among critical sections as algorithmic properties and quantifies them as a density computed over the number of executed instructions. The model abstracts from runtime aspects such as scheduling, the number of threads, and concurrency control used in a particular parallelization.