Table of contents
About this book
This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms.
This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.
- DOI https://doi.org/10.1007/978-981-15-3928-2
- Copyright Information Springer Nature Singapore Pte Ltd. 2020
- Publisher Name Springer, Singapore
- eBook Packages Computer Science Computer Science (R0)
- Print ISBN 978-981-15-3927-5
- Online ISBN 978-981-15-3928-2
- Series Print ISSN 2522-0179
- Series Online ISSN 2522-0187
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