  Numerical investigation of compressible flows faces two main challenges. In
order to accurately describe the flow characteristics, high-resolution
nonlinear numerical schemes are needed to capture discontinuities and resolve
wide convective, acoustic and interfacial scale ranges. The simulation of
realistic 3D problems with state-of-the-art FVM based on approximate Riemann
solvers with weighted nonlinear reconstruction schemes requires the usage of
HPC architectures. Efficient compression algorithms reduce computational and
memory load. Fully adaptive MR algorithms with LTS have proven their potential
for such applications. While modern CPU require multiple levels of parallelism
to achieve peak performance, the fine grained MR mesh adaptivity results in
challenging compute/communication patterns. Moreover, LTS incur for strong data
dependencies which challenge a parallelization strategy.
  We address these challenges with a block-based MR algorithm, where arbitrary
cuts in the underlying octree are possible. This allows for a parallelization
on distributed-memory machines via the MPI. We obtain neighbor relations by a
simple bit-logic in a modified Morton Order. The block-based concept allows for
a modular setup of the source code framework in which the building blocks of
the algorithm, such as the choice of the Riemann solver or the reconstruction
stencil, are interchangeable without loss of parallel performance. We present
the capabilities of the modular framework with a range of test cases and
scaling analysis with effective resolutions beyond one billion cells using
$\mathcal{O}(10^3)$ cores.
