  quest for processing speed potential. In fact, we always get a fraction of
the technically available computing power (so-called {\em theoretical peak}),
and the gap is likely to go hand-to-hand with the hardware complexity of the
target system. Among the key aspects of this complexity, we have: the {\em
heterogeneity} of the computing units, the {\em memory hierarchy and
partitioning} including the non-uniform memory access (NUMA) configuration, and
the {\em interconnect} for data exchanges among the computing nodes. Scientific
investigations and cutting-edge technical activities should ideally scale-up
with respect to sustained performance. The special case of quantitative
approaches for solving (large-scale) problems deserves a special focus. Indeed,
most of common real-life problems, even when considering the artificial
intelligence paradigm, rely on optimization techniques for the main kernels of
algorithmic solutions. Mathematical programming and pure combinatorial methods
are not easy to implement efficiently on large-scale supercomputers because of
{\em irregular control flow}, {\em complex memory access patterns}, {\em
heterogeneous kernels}, {\em numerical issues}, to name a few. We describe and
examine our thoughts from the standpoint of large-scale supercomputers.
