  Subgraph enumeration is a fundamental problem in graph analytics, which aims
to find all instances of a given query graph on a large data graph. In this
paper, we propose a system called HUGE to efficiently process subgraph
enumeration at scale in the distributed context. HUGE features 1) an optimiser
to compute an advanced execution plan without the constraints of existing
works; 2) a hybrid communication layer that supports both pushing and pulling
communication; 3) a novel two-stage execution mode with a lock-free and
zero-copy cache design, 4) a BFS/DFS-adaptive scheduler to bound memory
consumption, and 5) two-layer intra- and inter-machine load balancing. HUGE is
generic such that all existing distributed subgraph enumeration algorithms can
be plugged in to enjoy automatic speed up and bounded-memory execution.
