  We design and implement a distributed algorithm for balanced $k$-way
hypergraph partitioning that minimizes fanout, a fundamental hypergraph
quantity also known as the communication volume and ($k-1$)-cut metric, by
optimizing a novel objective called probabilistic fanout. This choice allows a
simple local search heuristic to achieve comparable solution quality to the
best existing hypergraph partitioners.
  Our algorithm is arbitrarily scalable due to a careful design that controls
computational complexity, space complexity, and communication. In practice, we
commonly process hypergraphs with billions of vertices and hyperedges in a few
hours. We explain how the algorithm's scalability, both in terms of hypergraph
size and bucket count, is limited only by the number of machines available. We
perform an extensive comparison to existing distributed hypergraph partitioners
and find that our approach is able to optimize hypergraphs roughly $100$ times
bigger on the same set of machines.
  We call the resulting tool Social Hash Partitioner (SHP), and accompanying
this paper, we open-source the most scalable version based on recursive
bisection.
