Fast Snapshottable Concurrent Braun Heaps

This paper proposes a new concurrent heap algorithm, based on a stateless shape property, which efficiently maintains balance during insert and removeMin operations implemented with hand-over-hand locking. It also provides a O(1) linearizable snapshot operation based on lazy copy-on-write semantics. Such snapshots can be used to provide consistent views of the heap during iteration, as well as to make speculative updates (which can later be dropped). The simplicity of the algorithm allows it to be easily proven correct, and the choice of shape property provides priority queue performance which is competitive with highly optimized skiplist implementations (and has stronger bounds on worst-case time complexity). A Scala reference implementation is provided.Comment: pre-print, submitted to DISC'1

Adapting Persistent Data Structures for Concurrency and Speculation

This work unifies insights from the systems and functional programming communities, in order to enable compositional reasoning about software which is nonetheless efficiently realizable in hardware. It exploits a correspondence between design goals for efficient concurrent data structures and efficient immutable persistent data structures, to produce novel implementations of mutable concurrent trees with low contention and an efficient snapshot operation to support speculative execution models. It also exploits commutativity to characterize a design space for integrating traditional high-performance concurrent data structures into Software Transactional Memory (STM) runtimes, and extends this technique to yield a novel algorithm for concurrent execution of so-called ``smart contracts'' (specialized programs which manipulate the state of blockchain ledgers).Comment: PhD Thesis, Brown University (2019