Feeding intelligence into synchronization primitives

This is a brief overview of my recent paper where I am reasoning about application of machine learning to synchronization methods, in particular, to spinlock protocols. Research around spinlocks is quite extensive, although machine learning techniques there are rare. As to my knowledge, there has been just one work so far that has been dealing with integration of machine leaning into spinlocks: Smartlocks: Self-aware Synchronization through Lock Acquisition Scheduling developed at MIT in 2009 by great researchers. The main idea of this work is to use performance metrics of the application that are measured via an API that sends heartbeats to the machine learning engine. The engine, then, works out an optimal policy which the threads utilize to intelligently organize their behavior.

My focus is on hybrid methods where threads should decide whether they should spin or park out. This decision is hard to make because of unpredictability of the system load (which is very crucial for the application performance) and number of threads that can ever contend for the lock. Perhaps, a way out can be to let the thread learn and decide themselves rather than hard code it. But they should decide about it to make it good both for themselves (to acquire the lock as fast as possible by burning as less as possible CPU cycles) and for the system (avoid making the system too oversubscribed). This sounds just like Russel Crowe starring as John Nash in “Beautiful Mind” exclaims “The best for the group comes when everyone in the group doing what’s best for himself and the group” :))). The feedback which can, for example, be modeled as wasted CPU cycles and/or scheduler load can be used to train the threads i.e. a thread sleeps for an amount of time, if lock is passed by, then the thread is punished, otherwise rewarded. All details are there in the paper.

Previously, I suggested to use machine learning techniques to eliminate some issues associated with spinlock protocols that employ blocking method as well. But the model that I presented left much room for improvement. This time, I tried to generalize it further and extend it to the case of the spin-then-park method as well (when submitted, a thread does just block or just spin but spins for a while then parks itself out).  Moreover, I’ve refined the model itself too. The full text can be found here. Hope you will enjoy.

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