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We prove that in asynchronous message-passing systems where at most one process may crash, there is no lock-free strongly linearizable implementation of a weak object that we call Test-or-Set (ToS). This object allows a single distinguished process to apply the set operation once, and a different distinguished process to apply the test operation also once. Since this weak object can be directly implemented by a single-writer single-reader (SWSR) register (and other common objects such as max-register, snapshot and counter), this result implies that there is no $1$-resilient lock-free strongly linearizable implementation of a SWSR register (and of these other objects) in message-passing systems. We also prove that there is no $1$-resilient lock-free emph{write} strongly-linearizable implementation of a 2-writer 1-reader (2W1R) register in asynchronous message-passing systems.
A key way to construct complex distributed systems is through modular composition of linearizable concurrent objects. A prominent example is shared registers, which have crash-tolerant implementations on top of message-passing systems, allowing the a
Collective communications, namely the patterns allgatherv, reduce_scatter, and allreduce in message-passing systems are optimised based on measurements at the installation time of the library. The algorithms used are set up in an initialisation phase
We investigate the minimal number of failures that can partition a system where processes communicate both through shared memory and by message passing. We prove that this number precisely captures the resilience that can be achieved by algorithms th
Message-passing models of distributed computing vary along numerous dimensions: degree of synchrony, kind of faults, number of faults... Unfortunately, the sheer number of models and their subtle distinctions hinder our ability to design a general th
Recently, Kabaila and Wijethunga assessed the performance of a confidence interval centred on a bootstrap smoothed estimator, with width proportional to an estimator of Efrons delta method approximation to the standard deviation of this estimator. Th