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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| Published in: | IEEE transactions on information theory Vol. 70; no. 5; pp. 3385 - 3398 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0018-9448, 1557-9654 |
| Online Access: | Get full text |
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| Abstract | 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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| AbstractList | 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. 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 [Formula Omitted] mappers and [Formula Omitted] reducers such that there is a unique reducer connected to any [Formula Omitted] 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. |
| Author | Elia, Petros Brunero, Federico |
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| SubjectTerms | 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 |
| Title | Multi-Access Distributed Computing |
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