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MMap: Fast Billion-Scale Graph Computation on a PC via Memory Mapping.

Zhiyuan Lin1, Minsuk Kahng1, Kaeser Md Sabrin1

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Summary
This summary is machine-generated.

Leveraging memory mapping (MMap), this study introduces a minimalist approach for efficient billion-node graph computation. This technique significantly outperforms existing methods, offering a new direction for scalable graph algorithm development.

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Area of Science:

  • Computer Science
  • Algorithm Design
  • Data Structures

Background:

  • Existing graph computation methods like GraphChi and TurboGraph achieve efficiency on large graphs using complex data structures and memory management.
  • These sophisticated techniques require significant engineering effort for high speed and scalability.

Purpose of the Study:

  • To propose and validate a minimalist approach for large-scale graph computation using memory mapping (MMap).
  • To demonstrate that MMap can achieve high performance and scalability without complex data structures.

Main Methods:

  • Implementation of graph algorithms using the memory mapping (MMap) capability of operating systems.
  • Evaluation of the MMap approach on billion-node scale graphs, including the 6.6 billion edge YahooWeb graph.

Main Results:

  • The MMap approach enables the implementation of scalable graph algorithms with significantly reduced code complexity.
  • Experiments show the MMap technique is faster or comparable to highly-optimized methods, achieving up to 9.5x speedup over GraphChi for PageRank computation on the Twitter graph.

Conclusions:

  • Memory mapping (MMap) is a viable and powerful technique for developing fast and scalable graph algorithms.
  • This minimalist MMap-based approach offers a promising new direction for the design and development of large-scale graph processing systems.