  High-performance analysis of unstructured data like graphs now is critical
for applications ranging from business intelligence to genome analysis. Towards
this, data centers hold large graphs in memory to serve multiple concurrent
queries from different users. Even a single analysis often explores multiple
options. Current computing architectures often are not the most time- or
energy-efficient solutions. The novel Lucata Pathfinder architecture tackles
this problem, combining migratory threads for low-latency reading with
memory-side processing for high-performance accumulation. One hundred to 750
concurrent breadth-first searches (BFS) all achieve end-to-end speed-ups of 81%
to 97% over one-at-a-time queries on a graph with 522M edges. Comparing to
RedisGraph running on a large Intel-based server, the Pathfinder achieves a
19$\times$ speed-up running 128 BFS queries concurrently. The Pathfinder also
efficiently supports a mix of concurrent analyses, demonstrated with connected
components and BFS.
