The MRG (MobilityReadingGroup) of Imperial College London invited Vasco Vasconcelos to give a seminar in September, 2017. The talk with the title “Deadlock Avoidance in Parallel Programs with Futures: Why parallel tasks should not wait for strangers” will address the work accepted to be presented at SPLASH/OOPSLA 2017, CORE A* Conference.
Futures are an elegant approach to expressing parallelism in functional programs. However, combining futures with imperative programming (as in C++ or in Java) can lead to pernicious bugs in the form of data races and deadlocks, as a consequence of uncontrolled data flow through mutable shared memory. In this talk we introduce the Known Joins (KJ) property for parallel programs with futures, and relate it to the Deadlock Freedom (DF) and the Data-Race Freedom (DRF) properties. We offer two key theoretical results: 1) DRF implies KJ, and 2) KJ implies DF. These results show that data-race freedom is sufficient to guarantee deadlock freedom in programs with futures that only manipulate unsynchronized shared variables. To the best of our knowledge, these are the first theoretical results to establish sufficient conditions for deadlock freedom in imperative parallel programs with futures, and to characterize the subset of data races that can trigger deadlocks (those that violate the KJ property). From result 2), we developed a tool that avoids deadlocks in linear time and space when KJ holds, i.e., when there are no data races among references to futures. When KJ fails, the tool reports the data race and optionally falls back to a standard deadlock avoidance algorithm by cycle detection. Our tool verified a dataset of ∼2,300 student’s homework solutions and found one deadlocked program. The performance results obtained from our tool are very encouraging: a maximum slowdown of 1.06× on a 16-core machine, always outperforming deadlock avoidance via cycle-detection. Proofs of the two main results were formalized using the Coq proof assistant.
[Joint work with Tiago Cogumbreiro, Rishi Surendran, Francisco Martins, Vivek Sarkar, and Max Grossman]