Distributed Memory Breadth-First Search Revisited: Enabling Bottom-p Search

TitleDistributed Memory Breadth-First Search Revisited: Enabling Bottom-p Search
Publication TypeConference Paper
Year of Publication2013
AuthorsBeamer, S., Buluc A., Asanović K., & Patterson D.
Conference NameIPDPS 2013
Abstract

Breadth-first search (BFS) is a fundamental graph
primitive frequently used as a building block for many complex
graph algorithms. In the worst case, the complexity of BFS is
linear in the number of edges and vertices, and the conventional
top-down approach always takes as much time as the worst case.
A recently discovered bottom-up approach manages to cut down
the complexity all the way to the number of vertices in the best
case, which is typically at least an order of magnitude less than
the number of edges. The bottom-up approach is not always
advantageous, so it is combined with the top-down approach
to make the direction-optimizing algorithm which adaptively
switches from top-down to bottom-up as the frontier expands.
We present a scalable distributed-memory parallelization of this
challenging algorithm and show up to an order of magnitude
speedups compared to an earlier purely top-down code. Our
approach also uses a 2D decomposition of the graph that has
previously been shown to be superior to a 1D decomposition.
Using the default parameters of the Graph500 benchmark, our
new algorithm achieves a performance rate of over 240 billion
edges per second on 115 thousand cores of a Cray XE6, which
makes it over 7 faster than a conventional top-down algorithm
using the same set of optimizations and data distribution.