Linearizable Implementations Do Not Suffice for Randomized Distributed Computation

TL;DR

Linearizability, while standard for correctness, is insufficient for randomized distributed algorithms, prompting the need for alternative correctness conditions.

Contribution

The paper demonstrates the limitations of linearizability in randomized distributed computation and explores alternative correctness criteria.

Findings

Linearizability does not guarantee correctness in randomized settings.

Alternative correctness conditions are necessary for randomized distributed algorithms.

The paper highlights fundamental limitations of current correctness standards.

Abstract

Linearizability is the gold standard among algorithm designers for deducing the correctness of a distributed algorithm using implemented shared objects from the correctness of the corresponding algorithm using atomic versions of the same objects. We show that linearizability does not suffice for this purpose when processes can exploit randomization, and we discuss the existence of alternative correctness conditions.