Abstract
Combining methods are highly effective for implementing concurrent queues and stacks. These data structures induce a heavy competition on one or two contention points. However, it was not known whether combining methods could be made effective for parallel scalable data structures that do not have a small number of contention points. In this paper, we introduce local combining on-demand, a new combining method for highly parallel data structures. The main idea is to apply combining locally for resources on which threads contend. We demonstrate the use of local combining on-demand on the common linked-list data structure. Measurements show that the obtained linked-list induces a low overhead when contention is low and outperforms other known implementations by up to 40% when contention is high.
This work was supported by the Israeli Science Foundation grant No. 275/14.
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Drachsler-Cohen, D., Petrank, E. (2014). LCD: Local Combining on Demand. In: Aguilera, M.K., Querzoni, L., Shapiro, M. (eds) Principles of Distributed Systems. OPODIS 2014. Lecture Notes in Computer Science, vol 8878. Springer, Cham. https://doi.org/10.1007/978-3-319-14472-6_24
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DOI: https://doi.org/10.1007/978-3-319-14472-6_24
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