Adaptive Hybrid Synchronization Primitives: A Reinforcement Learning Approach

The choice of synchronization primitive used to protect shared resources is a critical aspect of application performance and scalability, which has become extremely unpredictable with the rise of multicore machines. Neither of the most commonly used contention management strategies works well for all cases: spinning provides quick lock handoff and is attractive in an undersubscribed situation but wastes processor cycles in oversubscribed scenarios, whereas blocking saves processor resources and is preferred in oversubscribed cases but adds up to the critical path by lengthening the lock handoff phase. Hybrids, such as spin-then-block and spin-then-park, tackle this problem by switching between spinning and blocking depending on the contention level on the lock or the system load. Consequently, threads follow a fixed strategy and cannot learn and adapt to changes in system behavior. To this end, it is proposed to use principles of machine learning to formulate hybrid methods as a reinforcement learning problem that will overcome these limitations. In this way, threads can intelligently learn when they should spin or sleep. The challenges of the suggested technique and future work is also briefly discussed.