Scale Up or Scale Out for Graph Processing?

doi:10.1109/MIC.2018.032501520

2018-05-01

PRE

Jimmy Lin *

DOI：10.1109/MIC.2018.032501520

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摘要

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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?

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IEEE Internet Computing

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4.4

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2.0K

被引数:

2.0K

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University of Waterloo

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2.2W

论文数: 2.3W

被引数: 3.3W

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