A Scalable General Purpose System for Large-Scale Graph Processing

Jiawen Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Graph analytics is an important and computationally demanding class of data analytics. It is essential to balance scalability, ease-of-use and high performance in large scale graph analytics. As such, it is necessary to hide the complexity of parallelism, data distribution and memory locality behind an abstract interface. The aim of this work is to build a scalable graph analytics framework that does not demand significant parallel programming experience based on NUMA-awareness.
The realization of such a system faces two key problems:
(i)~how to develop a scale-free parallel programming framework that scales efficiently across NUMA domains; (ii)~how to efficiently apply graph partitioning in order to create separate and largely independent work items that can be distributed among threads.
Original languageEnglish
Title of host publicationPACT '16:Proceedings of the 25th international conference on Parallel architectures and compilation
Number of pages1
Publication statusPublished - 11 Sep 2016
EventPACT2016 : The 25th International Conference on Parallel Architectures and Compilation Techniques - Israel , Haifa, Israel
Duration: 11 Sep 201615 Sep 2016


Abbreviated titleStudent Research Competition
Internet address


  • Large scale graph processing
  • scalable parallel programming


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