Abstract
In many scientific and computational domains, graphs are used to represent and analyze data. Such graphs often exhibit the characteristics of small-world networks: few high-degree vertexes connect many low-degree vertexes. Despite the randomness in a graph search, it is possible to capitalize on this characteristic and cache relevant information in high-degree vertexes. We applied this idea by caching remote vertex ids in a parallel breadth-first search implementation, and demonstrated 1.6x to 2.4x speedup over the reference implementation on 64 to 1024 cores. We proposed a system design in which resources are dedicated exclusively to caching, and shared among a set of nodes. Our evaluation demonstrates that this design has the potential to reduce communication and improve performance over large scale systems. Finally, we used a memcached system as the cache server finding that a generic protocol that does not match the usage semantics may hinder the potential performance improvements.
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