imulation and modeling for performance predic-
tion and profiling is essential for developing and maintaining
HPC code that is expected to scale for next-generation exascale
systems, and correctly modeling network behavior is essential for
creating realistic simulations. In this article we describe an im-
plementation of a flow-based hybrid network model that accounts
for factors such as network topology and contention, which are
commonly ignored by other approaches. We focus on large-scale,
Ethernet-connected systems, as these currently compose 37.8%
of the TOP500 index, and this share is expected to increase
as higher-speed 10 and 100GbE become more available. The
European Mont-Blanc project to study exascale computing by de-
veloping prototype systems with low-power embedded devices will
also use Ethernet-based interconnect. Our model is implemented
within SMPI, an open-source MPI implementation that connects
real applications to the SimGrid simulation framework. SMPI
provides implementations of collective communications based
on current versions of both OpenMPI and MPICH. SMPI and
SimGrid also provide methods for easing the simulation of large-
scale systems, including shadow execution, memory folding, and
support for both online and offline (i.e., post-mortem) simulation.
We validate our proposed model by comparing traces produced
by SMPI with those from real world experiments, as well as
with those obtained using other established network models.
Our study shows that SMPI has a consistently better predictive
power than classical LogP-based models for a wide range of
scenarios including both established HPC benchmarks and real
applications.