Automatic memory management system for automatic parallelization

Abstract: With Moore’s law coming to an end and the era of multiprocessor chips emerging, the need for ways of dealing with the essential problems with concurrency is becoming imminent. Automatic parallelization for imperative languages and pure functions in functional programming languages all try to prove independence statically. This thesis argues that independence is dynamic in nature. Static analysis for automatic parallelization has failed to do anything but trivial optimizations. This thesis shows a new approach where dynamic analysis about the system is provided for very low costs using a garbage collector that has to go through all live cells anyway. Immutable sub-graphs of objects that cannot change state are found. Their methods become pure functions that can be parallelized. The garbage collector implemented is a kind of replicating collector. It is about three times faster than Boehm’s collector in garbage collection, fully concurrent and provides the dynamic analysis almost for free.