Abstract
Establishing the scalability of a concurrent algorithm a priori, before implementing and evaluating it on a concrete multi-core platform, seems difficult, if not impossible. In the context of search data structures however, according to all practical work of the past decade, algorithms that scale share a common characteristic: They all resemble standard sequential implementations for their respective data structure type and strive to minimize the number of synchronization operations.
In this paper, we present sequential proximity, a theoretical framework to determine whether a concurrent search algorithm is close to its sequential counterpart. With sequential proximity we take the first step towards a theory of scalability for concurrent search algorithms.
This work has been supported in part by the European ERC Grant 339539 - AOC.
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Notes
- 1.
Locations containing pointers could be differentiated from other locations if they contain a pointer type. This could be easily done by for example marking the last bit of the value residing in such a location.
- 2.
For randomized data structures, such as skip lists [18], we assume that the underlying random number generator produces the exact same sequences of numbers for both \(Prog_S\) and \(Prog_C\).
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Antoniadis, K., Guerraoui, R., Stainer, J., Trigonakis, V. (2017). Sequential Proximity. In: El Abbadi, A., Garbinato, B. (eds) Networked Systems. NETYS 2017. Lecture Notes in Computer Science(), vol 10299. Springer, Cham. https://doi.org/10.1007/978-3-319-59647-1_30
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