  Parallel code design is a challenging task especially when addressing
petascale systems for massive parallel processing (MPP), i.e. parallel
computations on several hundreds of thousands of cores. An in-house
computational fluid dynamics code, developed by our group, was designed for
such high-fidelity runs in order to exhibit excellent scalability values. Basis
for this code is an adaptive hierarchical data structure together with an
efficient communication and (numerical) computation scheme that supports MPP.
For a detailled scalability analysis, we performed several experiments on two
of Germany's national supercomputers up to 140,000 processes. In this paper, we
will show the results of those experiments and discuss any bottlenecks that
could be observed while solving engineering-based problems such as porous media
flows or thermal comfort assessments for problem sizes up to several hundred
billion degrees of freedom.
