Distributed Computing: Fundamentals, Simulations and Advanced Topics

By M. Paprzycki. 2001

991 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Hardware and Architecture

Citations in the corpus

Distributed computation in dynamic networks (2010, background): “, resilience to a bounded number of faults, has been at the core of distributed computing research from its very beginning [5, 34].”
Concurrency and Privacy with Payment-Channel Networks (2017, background): “We consider the bounded synchronous communication setting [23].”
Leader election algorithms for mobile ad hoc networks (2000, background): “The standard definition of the leader election problem for static networks [1] is that”
Digital ecosystems in the clouds: Towards community cloud computing (2009, background): “a) Distributed Computation: The field has a successful history of centrally controlled incarnations [92].”
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory (2018, methodology): “We consider a standard asynchronous shared-memory model [2], in which n threads (or processes) P1, .”
All You Need is DAG (2021, methodology): “To ensure this, we borrow the technique from the common-core abstraction [2], which guarantees that after three rounds of all-to-all sending and collecting accumulated sets of values, all correct processes have at least 2f + 1 common values.”
A Survey of Distributed Consensus Protocols for Blockchain Networks (2019, background): “Three levels of synchrony, namely synchronous, partially synchronous, and asynchronous, are often assumed in the literature [22], [25].”
Blockchain Consensus Protocols in the Wild (2017, background): “The topic has evolved through a long history since and is covered in many textbooks [4, 18, 59, 73]; a good summary can be found in a “30-year perspective on replication” [25].”
Graph Partition Neural Networks for Semi-Supervised Classification (2018, background): “The simplest approach, and the one adopted by almost all graph-based neural networks is to follow synchronous message-passing systems [3] from distributed computing theory.”
Distributed consensus of discrete‐time multi‐agent systems with multiplicative noises (2015, background): “Due to its wide applications in many areas such as formation control [1], distributed sensor fusion [2, 3], cooperative target search and exploration [4], distributed mission planning [5], and distributed computing [6], distributed consensus of multi-agent networks has attracted more and more attent”