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The theory of distributed computing, lagging in its development behind practice, has been biased in its modelling by employing mechanisms within the model mimicking reality. Reality means, processors can fail. But theory is about predicting consequences of reality, hence if we capture reality by artificial models, but those nevertheless make analysis simpler, we should pursue the artificial models. Recently the idea was advocated to analyze distributed systems and view processors as infallible. It is the message delivery substrate that causes problems. This view not only can effectively emulate reality, but above all seems to allow to view any past models as emph{synchronous} models. Synchronous models are easier to analyze than asynchronous ones. Furthermore, it gives rise to models we havent contemplated in the past. One such model, presented here, is the Hybrid Message-Adversary. We motivate this model through the need to analyze Byzantine faults. The Hybrid model exhibits a phenomenon not seen in the past.
Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony. In contrast, the s
We present new protocols for Byzantine state machine replication and Byzantine agreement in the synchronous and authenticated setting. The celebrated PBFT state machine replication protocol tolerates $f$ Byzantine faults in an asynchronous setting us
Consider an arbitrary network of communicating modules on a chip, each requiring a local signal telling it when to execute a computational step. There are three common solutions to generating such a local clock signal: (i) by deriving it from a singl
In this paper we will present the Multidimensional Byzantine Agreement (MBA) Protocol, a leaderless Byzantine agreement protocol defined for complete and synchronous networks that allows a network of nodes to reach consensus on a vector of relevant i
We design and implement a distributed multinode synchronous SGD algorithm, without altering hyper parameters, or compressing data, or altering algorithmic behavior. We perform a detailed analysis of scaling, and identify optimal design points for dif