  The supercomputing platforms available for high performance computing based
research evolve at a great rate. However, this rapid development of novel
technologies requires constant adaptations and optimizations of the existing
codes for each new machine architecture. In such context, minimizing time of
efficiently porting the code on a new platform is of crucial importance. A
possible solution for this common challenge is to use simulations of the
application that can assist in detecting performance bottlenecks. Due to
prohibitive costs of classical cycle-accurate simulators, coarse-grain
simulations are more suitable for large parallel and distributed systems. We
present a procedure of implementing the profiling for openQCD code [1] through
simulation, which will enable the global reduction of the cost of profiling and
optimizing this code commonly used in the lattice QCD community. Our approach
is based on well-known SimGrid simulator [2], which allows for fast and
accurate performance predictions of HPC codes. Additionally, accurate
estimations of the program behavior on some future machines, not yet accessible
to us, are anticipated.
