  In this paper we would like to share our experience for transforming a
parallel code for a Computational Fluid Dynamics (CFD) problem into a parallel
version for the RedisDG workflow engine. This system is able to capture
heterogeneous and highly dynamic environments, thanks to opportunistic
scheduling strategies. We show how to move to the field of "HPC as a Service"
in order to use heterogeneous platforms. We mainly explain, through the CFD use
case, how to transform the parallel code and we exhibit challenges to 'unfold'
the task graph dynamically in order to improve the overall performance (in a
broad sense) of the workflow engine. We discuss in particular of the impact on
the workflow engine of such dynamic feature. This paper states that new models
for High Performance Computing are possible, under the condition we revisit our
mind in the direction of the potential of new paradigms such as cloud, edge
computing.
