  Here, we test the performance and scalability of fully-asynchronous,
best-effort communication on existing, commercially-available HPC hardware.
  A first set of experiments tested whether best-effort communication
strategies can benefit performance compared to the traditional perfect
communication model. At high CPU counts, best-effort communication improved
both the number of computational steps executed per unit time and the solution
quality achieved within a fixed-duration run window.
  Under the best-effort model, characterizing the distribution of quality of
service across processing components and over time is critical to understanding
the actual computation being performed. Additionally, a complete picture of
scalability under the best-effort model requires analysis of how such quality
of service fares at scale. To answer these questions, we designed and measured
a suite of quality of service metrics: simulation update period, message
latency, message delivery failure rate, and message delivery coagulation. Under
a lower communication-intensivity benchmark parameterization, we found that
median values for all quality of service metrics were stable when scaling from
64 to 256 process. Under maximal communication intensivity, we found only minor
-- and, in most cases, nil -- degradation in median quality of service.
  In an additional set of experiments, we tested the effect of an apparently
faulty compute node on performance and quality of service. Despite extreme
quality of service degradation among that node and its clique, median
performance and quality of service remained stable.
