Dynamic prediction of collection yield for managed runtimes

The growth in complexity of modern systems makes it increasingly difficult to extract high-performance. The software stacks for such systems typically consist of multiple layers and include managed runtime environments (MREs). In this paper, we investigate techniques to improve cooperation between these layers and the hardware to increase the efficacy of automatic memory management in MREs. General-purpose MREs commonly implement parallel and/or concurrent garbage collection and employ compaction to eliminate heap fragmentation. Moreover, most systems trigger collection based on the amount of heap a program uses. Our analysis shows that in many cases this strategy leads to ineffective collections that are unable to reclaim sufficient space to justify the incurred cost. To avoid such collections, we exploit the observation that dead objects tend to cluster together and form large, never-referenced, regions in the address space that correlate well with virtual pages that have not recently been referenced by the application. We leverage this correlation to design a new, simple and light-weight, yield predictor that estimates the amount of reclaimable space in the heap using hardware page reference bits. Our predictor allows MREs to avoid low-yield collections and thereby improve resource management. We integrate this predictor into three state-of-the-art parallel compactors, implemented in the HotSpot JVM, that represent distinct canonical heap layouts. Our empirical evaluation, based on standard Java benchmarks and open-source applications, indicates that inexpensive and accurate yield prediction can improve performance significantly.