  Supercomputers worldwide provide the necessary infrastructure for
groundbreaking research. However, most supercomputers are not designed equally
due to different desired figure of merit, which is derived from the
computational bounds of the targeted scientific applications' portfolio. In
turn, the design of such computers becomes an optimization process that strives
to achieve the best performances possible in a multi-parameters search space.
Therefore, verifying and evaluating whether a supercomputer can achieve its
desired goal becomes a tedious and complex task. For this purpose, many full,
mini, proxy, and benchmark applications have been introduced in the attempt to
represent scientific applications. Nevertheless, as these benchmarks are hard
to expand, and most importantly, are over-simplified compared to scientific
applications that tend to couple multiple scientific domains, they fail to
represent the true scaling capabilities. We suggest a new physical scalable
benchmark framework, namely ScalSALE, based on the well-known SALE scheme.
ScalSALE's main goal is to provide a simple, flexible, scalable infrastructure
that can be easily expanded to include multi-physical schemes while maintaining
scalable and efficient execution times. By expanding ScalSALE, the gap between
the over-simplified benchmarks and scientific applications can be bridged. To
achieve this goal, ScalSALE is implemented in Modern Fortran with simple OOP
design patterns and supported by transparent MPI-3 blocking and non-blocking
communication that allows such a scalable framework. ScalSALE is compared to
LULESH via simulating the Sedov-Taylor blast wave problem using strong and weak
scaling tests. ScalSALE is executed and evaluated with both rezoning options -
Lagrangian and Eulerian.
