Partitioning-Aware Performance Modeling of Distributed Graph Processing Tasks

Much of the data being produced in large scale by modern applications represents connected entities and their relationships, that can be modeled as large graphs. In order to extract valuable information from these large datasets, several parallel and distributed graph processing engines have been pr...

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Bibliographic Details
Published in:	International journal of parallel programming Vol. 51; no. 4-5; pp. 231 - 255
Main Authors:	Presser, Daniel, Siqueira, Frank
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.10.2023 Springer Nature B.V
Subjects:	Algorithms Computer Science Datasets Graphs Model accuracy Partitioning Performance prediction Prediction models Processor Architectures Software Engineering/Programming and Operating Systems Task scheduling Theory of Computation Workers Performance modeling Distributed graph processing Scheduling
ISSN:	0885-7458, 1573-7640
Online Access:	Get full text
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