DistTC: High performance distributed triangle counting
2019 IEEE High Performance Extreme Computing Conference (HPEC), 2019•ieeexplore.ieee.org
We describe a novel multi-machine multi-GPU implementation of triangle counting which
exploits a novel application-agnostic graph partitioning strategy that eliminates almost all
inter-host communication during triangle counting. Experimental results show that this
distributed triangle counting implementation can handle very large graphs such as
clueweb12, which has almost one billion vertices and 37 billion edges, and it is up to 1.6×
faster than TriCore, the 2018 Graph Challenge champion.
exploits a novel application-agnostic graph partitioning strategy that eliminates almost all
inter-host communication during triangle counting. Experimental results show that this
distributed triangle counting implementation can handle very large graphs such as
clueweb12, which has almost one billion vertices and 37 billion edges, and it is up to 1.6×
faster than TriCore, the 2018 Graph Challenge champion.
We describe a novel multi-machine multi-GPU implementation of triangle counting which exploits a novel application-agnostic graph partitioning strategy that eliminates almost all inter-host communication during triangle counting. Experimental results show that this distributed triangle counting implementation can handle very large graphs such as clueweb12, which has almost one billion vertices and 37 billion edges, and it is up to 1.6× faster than TriCore, the 2018 Graph Challenge champion.
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