Scale Up or Scale Out for Graph Processing?

This column explores a simple question: scale up or scale out for graph processing? Should we simply throw beefier individual multi-core, large-memory machines at graph processing tasks and focus on developing more efficient multi-threaded algorithms, or are investments in distributed graph processi...

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Vydáno v:IEEE internet computing Ročník 22; číslo 3; s. 72 - 78
Hlavní autor: Lin, Jimmy
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.05.2018
Témata:
Big Data
graph processing
Graphical user interfaces
Internet
Memory management
Random access memory
ISSN:1089-7801, 1941-0131
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Shrnutí:This column explores a simple question: scale up or scale out for graph processing? Should we simply throw beefier individual multi-core, large-memory machines at graph processing tasks and focus on developing more efficient multi-threaded algorithms, or are investments in distributed graph processing frameworks and accompanying algorithms worthwhile? For rhetorical convenience, I adopt customary definitions, referring to the former as scale up and the latter as scale out. Under what circumstances should we prefer one approach over the other?
ISSN:1089-7801
1941-0131
DOI:10.1109/MIC.2018.032501520