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The multicore revolution means that programmers have many cores at their disposal in everything from phones to large server systems. Concurrent data structures are needed to make good use of all the cores. Designing a concurrent data structure that performs well across many different scenarios is a difficult task. The reason for this is that the best synchronization granularity and data organization vary between scenarios. Furthermore, the number of parallel threads and the types of operations that are accessing a data structure may even change over time.

This dissertation tackles the problem mentioned above by proposing concurrent data structures that dynamically adapt their synchronization granularity and organization based on usage statistics collected at run-time. Two of these data structures (one lock-free and one lock-based) implement concurrent sets with support for range queries and other multi-item operations. These data structures adapt their synchronization granularity based on detected contention and the number of items that are involved in multi-item operations such as range queries. This dissertation also proposes a concurrent priority queue that dynamically changes its precision based on detected contention.

Experimental evaluations of the proposed data structures indicate that they outperform non-adaptive data structures over a wide range of scenarios because they adapt their synchronization based on usage statistics. Possible practical consequences of the work described in this dissertation are faster parallel programs and a reduced need to manually tune the synchronization granularities of concurrent data structures.