- Abstract: Graph computation approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can perform efficient computation on billion-node graphs. To achieve high speed and scalability, they often need sophisticated data structures and memory management strategies.Learn more:Abstract: Graph computation approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can perform efficient computation on billion-node graphs. To achieve high speed and scalability, they often need sophisticated data structures and memory management strategies.ieeexplore.ieee.org/abstract/document/7004226We propose a minimalist approach that forgoes such complexities, by leveraging the fundamental memory mapping (MMap) capability found on operating systems.repository.gatech.edu/handle/1853/49226Our curiosity led us to investigate if memory mapping can be a viable technique to support fast, scalable graph computation. In this paper, we present our major contributions and results:repository.gatech.edu/server/api/core/bitstreams/7…
MMap: Fast billion-scale graph computation on a PC via memory …
Abstract: Graph computation approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can perform efficient computation on billion-node graphs. To …
We describe our fast and minimal MMap approach for large graph computation. We will explain how MMap uses simpler data structures for storing and accessing graph edges and how …
MMap: Fast billion-scale graph computation on a PC via memory …
Oct 1, 2014 · This paper proposes a general, disk-based graph engine called TurboGraph to process billion-scale graphs very efficiently by using modern hardware on a single PC and …
We contribute our crucial insight that memory mapping, a fun-damental capability from operating systems (OSes), is a viable technique for creating fast, scalable graph algorithms that sur …
MMap: Fast Billion-Scale Graph Computation on a PC via Memory …
Graph computation approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can perform efficient computation on billion-node graphs. To achieve high speed …
- Author: Zhiyuan Lin, Minsuk Kahng, Kaeser Md. Sabrin, Duen Horng Polo Chau, Ho Lee, U Kang
- DOI: 10.1109/BigData.2014.7004226
- Publish Year: 2014
- Published: 2014/10
Mmap: Fast billion-scale graph computation on a pc via memory …
Oct 27, 2014 · In this paper, we propose FlexGraph, a scalable distributed graph mining method reducing the costs by exploiting properties of real-world graphs. FlexGraph significantly …
Mmap: Fast Billion-Scale Graph Computation on a PC …
Abstract—Graph computation approaches such as GraphChi PageRank Runtime on Twitter Graph and TurboGraph recently demonstrated that a single PC can (1.5 billion edges; 10 iterations) perform efficient computation on billion-node …
MMAP: Mining Billion-Scale Graphs on a PC with Fast, Minimalist ...
Large graphs with billions of nodes and edges are increasingly common, calling for new kinds of scalable computation frameworks. State-of-the-art approaches such as GraphChi and …
State-of-the-art approaches such as GraphChi and TurboGraph recently have demonstrated that a single machine can efficiently perform advanced computation on billion-node graphs. …
MMap: Fast Billion-Scale Graph Computation on a PC via Memory …
Graph computation approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can perform efficient computation on billion-node graphs. To achieve high speed …
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