Abstract: Garbage collection (GC) to reclaim memory in computing systems sometimes suspends applications, web services, and other mutator programs in order to scan their execution stacks, which reduces their responsiveness. But suspension times are decreased by behavior-driven stack scan optimization (BDSSO) functionality that increases the concurrency of mutator execution with GC stack scanning. BDSSO obtains execution stack frame occurrence data, determines frame execution likelihoods, selects a stack scan depth based on the likelihoods, and installs a scan return barrier at the selected depth. Then the GC scans the execution stack below the barrier while the mutator runs, thus increasing concurrency and improving mutator responsiveness. Selected barrier locations vary according to actual stack activity to provide optimized concurrency instead of using an inflexible approach to barrier placement. Existing profiler samples or virtual machine interfaces can be reused by BDSSO.

Abstract: Garbage collection (GC) to reclaim memory in computing systems sometimes suspends applications, web services, and other mutator programs in order to scan their execution stacks, which reduces their responsiveness. But suspension times are decreased by behavior-driven stack scan optimization (BDSSO) functionality that increases the concurrency of mutator execution with GC stack scanning. BDSSO obtains execution stack frame occurrence data, determines frame execution likelihoods, selects a stack scan depth based on the likelihoods, and installs a scan return barrier at the selected depth. Then the GC scans the execution stack below the barrier while the mutator runs, thus increasing concurrency and improving mutator responsiveness. Selected barrier locations vary according to actual stack activity to provide optimized concurrency instead of using an inflexible approach to barrier placement. Existing profiler samples or virtual machine interfaces can be reused by BDSSO.