Multi-Access Distributed Computing

Coded distributed computing (CDC) is a new technique proposed with the purpose of decreasing the intense data exchange required for parallelizing distributed computing systems. Under the famous MapReduce paradigm, this coded approach has been shown to decrease this communication overhead by a factor...

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Bibliographic Details
Published in:IEEE transactions on information theory Vol. 70; no. 5; pp. 3385 - 3398
Main Authors: Brunero, Federico, Elia, Petros
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Coded distributed computing
coded multicasting
Combinatorial analysis
Communication
communication complexity
communication load
Computational modeling
Computer networks
Costs
Data exchange
Distributed computing
Distributed processing
Information theory
information-theoretic converse
Load modeling
MapReduce
Measurement
multi-access distributed computing (MADC)
Network topologies
Nodes
Parallel processing
Topology
ISSN:0018-9448, 1557-9654
Online Access:Get full text
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Summary:Coded distributed computing (CDC) is a new technique proposed with the purpose of decreasing the intense data exchange required for parallelizing distributed computing systems. Under the famous MapReduce paradigm, this coded approach has been shown to decrease this communication overhead by a factor that is linearly proportional to the overall computation load during the mapping phase. In this paper, we propose multi-access distributed computing (MADC) as a generalization of the original CDC model, where now mappers (nodes in charge of the map functions) and reducers (nodes in charge of the reduce functions) are distinct computing nodes that are connected through a multi-access network topology. Focusing on the MADC setting with combinatorial topology, which implies <inline-formula> <tex-math notation="LaTeX">\Lambda </tex-math></inline-formula> mappers and <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> reducers such that there is a unique reducer connected to any <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> mappers, we propose a coded scheme and an information-theoretic converse, which jointly identify the optimal inter-reducer communication load, as a function of the computation load, to within a constant gap of 1.5. Additionally, a modified coded scheme and converse identify the optimal max-link communication load across all existing links to within a gap of 4.
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ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2024.3373128