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Distributed Big Graph Caching

Zichao Qi, Yanghua Xiao, Bin Shao, and Haixun Wang


Distributed systems have the potential to support big graphs. A distributed system can scale out to hold graphs of any size. Distributed systems also have the parallelized computing power to boost the performance of big graph computation. To deploy a big graph in a distributed system, we must first partition the big graph into different parts (typically by random hashing) each of which is loaded to a single machine's memory. Then, all succeeding computations will run in distributed memories.


Publication typeTechReport
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