We describe a non-blocking concurrent hash trie based on shared-memory single-word compare-and-swap instructions. The hash trie supports standard mutable lock-free operations such as insertion, removal, lookup and their conditional variants. To ensure space-efficiency, removal operations compress the trie when necessary. We show how to implement an efficient lock-free snapshot operation for concurrent hash tries. The snapshot operation uses a single-word compare-and-swap and avoids copying the data structure eagerly. Snapshots are used to implement consistent iterators and a linearizable size retrieval. We compare concurrent hash trie performance with other concurrent data structures and evaluate the performance of the snapshot operation.

As the number of cores increases in modern multiprocessors, it is becoming increasingly dicult to write general purpose applications that eciently utilize this computing power. This remains an open research problem.Most applications manipulate structured data. Modern languages and platforms provide collection frameworks with basic data structures like lists, hashtables and trees. These data structures have a range of predened operations which include mapping, ltering or nding elements. Such bulk operations traverse the collection and process the elements sequentially. Their implementation relies on iterators, which are not applicable to parallel operations due to their sequential nature.We present an approach to parallelizing collection operations in a generic way, used to factor out common parallel operations in collection libraries. Our framework is easy to use and straightforward to extend to new collections. We show how to implement concrete parallel collections such as parallel arrays and parallel hash maps, proposing an ecient solution to parallel hash map construction. Finally, we give benchmarks showing the performance of parallel collection operations.

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Abstract. This paper describes an implementation of a non-blocking concurrent hash trie based on single-word compare-and-swap instructions in a shared-memory system. Insert, lookup and remove operations modifying dierent parts of the hash trie can be run completely independently. Remove operations ensure that the unneeded memory is freed and that the trie is kept compact. A pseudocode for these operations is presented and a proof of correctness is given we show that the implementation is linearizable and lock-free. Finally, benchmarks are presented that compare concurrent hash trie operations against the corresponding operations on other concurrent data structures.

State-of-the-art immutable collections have wildly differing performance characteristics across their operations, often forcing programmers to choose different collection implementations for each task. Thus, changes to the program can invalidate the choice of collections, making code evolution costly. It would be desirable to have a collection that performs well for a broad range of operations.To this end, we present the RRB-Vector, an immutable sequence collection that offers good performance across a large number of sequential and parallel operations. The underlying innovations are: (1) the Relaxed-Radix-Balanced (RRB) tree structure, which allows efficient structural reorganization, and (2) an optimization that exploits spatio-temporal locality on the RRB data structure in order to offset the cost of traversing the tree.In our benchmarks, the RRB-Vector speedup for parallel operations is lower bounded by 7× when executing on 4 CPUs of 8 cores each. The performance for discrete operations, such as appending on either end, or updating and removing elements, is consistently good and compares favorably to the most important immutable sequence collections in the literature and in use today. The memory footprint of RRB-Vector is on par with arrays and an order of magnitude less than competing collections.