Modern transactional response-time sensitive applications have run into practical limits on the size of garbage collected heaps. The heap can only grow until GC pauses exceed the response-time limits. Sustainable, scalable concurrent collection has become a feature worth paying for.Azul Systems has built a custom system (CPU, chip, board, and OS) specifically to run garbage collected virtual machines. The custom CPU includes a read barrier instruction. The read barrier enables a highly concurrent (no stop-the-world phases), parallel and compacting GC algorithm. The Pauseless algorithm is designed for uninterrupted application execution and consistent mutator throughput in every GC phase.Beyond the basic requirement of collecting faster than the allocation rate, the Pauseless collector is never in a rush to complete any GC phase. No phase places an undue burden on the mutators nor do phases race to complete before the mutators produce more work. Portions of the Pauseless algorithm also feature a self-healing behavior which limits mutator overhead and reduces mutator sensitivity to the current GC state.We present the Pauseless GC algorithm, the supporting hardware features that enable it, and data on the overhead, efficiency, and pause times when running a sustained workload.

We believe that optimizing compilers should treat the machine-independent optimizations (e.g., conditional constant propagation, global value numbering) and code motion issues separately.’ Removing the code motion requirements from the machine-independent optimization allows stronger optimizations using simpler algorithms. Preserving a legal schedule is one of the prime sources of complexity in algorithms like PRE [18, 13] or global congruence finding [2, 20].

Modern optimizing compilers use several passes over a program's intermediate representation to generate good code. Many of these optimizations exhibit a phase-ordering problem. Getting the best code may require iterating optimizations until a fixed point is reached. Combining these phases can lead to the discovery of more facts about the program, exposing more opportunities for optimization. This article presents a framework for describing optimizations. It shows how to combine two such frameworks and how to reason about the properties of the resulting framework. The structure of the frame work provides insight into when a combination yields better results. To make the ideas more concrete, this article presents a framework for combining constant propagation, value numbering, and unreachable-code elimination. It is an open question as to what other frameworks can be combined in this way.