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• W607233492 abstract "Recent graph computation approaches such as GraphChi, X-Stream, TurboGraph and MMap demonstrated that a single PC can perform ecient computation on billion scale graphs. While they use dierent techniques to achieve scalability through opti- mizing I/O operations, such optimization often does not fully exploit the capabilities of modern hard drives. We contribute: (1) a novel and scalable graph computation framework called M-Flash that uses a block partition model to boost computation speed and reduce disk ac- cesses, by logically dividing a graph and its node data into blocks that can fully t in RAM for reuse; (2) a exible and deliberatively simple programming model, as part of M-Flash, that enables us to implement pop- ular and essential graph algorithms, including the rst single-machine billion-scale eigensolver; and (3) exten- sive experiments on real graphs with up to 6.6 billion edges, demonstrating M-Flash's consistent and signi- cant speed-up against state-of-the-art approaches." @default.
• W607233492 created "2016-06-24" @default.
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• W607233492 date "2015-06-03" @default.
• W607233492 modified "2023-09-24" @default.
• W607233492 title "M-Flash: Fast Billion-scale Graph Computation Using Block Partition Model" @default.
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