  We study the performance behaviour of a seismic simulation using the ExaHyPE
engine with a specific focus on memory characteristics and energy needs.
ExaHyPE combines dynamically adaptive mesh refinement (AMR) with ADER-DG. It is
parallelized using tasks, and it is cache efficient. AMR plus ADER-DG yields a
task graph which is highly dynamic in nature and comprises both arithmetically
expensive tasks and tasks which challenge the memory's latency. The expensive
tasks and thus the whole code benefit from AVX vectorization, though we suffer
from memory access bursts. A frequency reduction of the chip improves the
code's energy-to-solution. Yet, it does not mitigate burst effects. The bursts'
latency penalty becomes worse once we add Intel Optane technology, increase the
core count significantly, or make individual, computationally heavy tasks fall
out of close caches. Thread overbooking to hide away these latency penalties
contra-productive with non-inclusive caches as it destroys the cache and
vectorization character. In cases where memory-intense and computationally
expensive tasks overlap, ExaHyPE's cache-oblivious implementation can exploit
deep, non-inclusive, heterogeneous memory effectively, as main memory misses
arise infrequently and slow down only few cores. We thus propose that upcoming
supercomputing simulation codes with dynamic, inhomogeneous task graphs are
actively supported by thread runtimes in intermixing tasks of different compute
character, and we propose that future hardware actively allows codes to
downclock the cores running particular task types.
