# Efficient Randomized Test-And-Set Implementations

## TL;DR

This paper introduces efficient randomized test-and-set implementations using group election, improving expected step complexities and space efficiency, and provides bounds on process steps under certain adversaries.

## Contribution

It presents new TAS algorithms with improved expected max-step complexities and space efficiency, and introduces the group election problem as a key component.

## Key findings

- Expected max-step complexity $O(\log^ extasteriskcentered k)$ in location-oblivious model
- Expected max-step complexity $O(\log\log k)$ against read/write-oblivious adversary
- Space complexity reduced from super-linear to linear in a modified TAS algorithm

## Abstract

We study randomized test-and-set (TAS) implementations from registers in the asynchronous shared memory model with n processes. We introduce the problem of group election, a natural variant of leader election, and propose a framework for the implementation of TAS objects from group election objects. We then present two group election algorithms, each yielding an efficient TAS implementation. The first implementation has expected max-step complexity $O(\log^\ast k)$ in the location-oblivious adversary model, and the second has expected max-step complexity $O(\log\log k)$ against any read/write-oblivious adversary, where $k\leq n$ is the contention. These algorithms improve the previous upper bound by Alistarh and Aspnes [2] of $O(\log\log n)$ expected max-step complexity in the oblivious adversary model. We also propose a modification to a TAS algorithm by Alistarh, Attiya, Gilbert, Giurgiu, and Guerraoui [5] for the strong adaptive adversary, which improves its space complexity from super-linear to linear, while maintaining its $O(\log n)$ expected max-step complexity. We then describe how this algorithm can be combined with any randomized TAS algorithm that has expected max-step complexity $T(n)$ in a weaker adversary model, so that the resulting algorithm has $O(\log n)$ expected max-step complexity against any strong adaptive adversary and $O(T(n))$ in the weaker adversary model. Finally, we prove that for any randomized 2-process TAS algorithm, there exists a schedule determined by an oblivious adversary such that with probability at least $(1/4)^t$ one of the processes needs at least t steps to finish its TAS operation. This complements a lower bound by Attiya and Censor-Hillel [7] on a similar problem for $n\geq 3$ processes.