  The proliferation of GPUs and accelerators in recent supercomputing systems,
so called heterogeneous architectures, has led to increased complexity in
execution environments and programming models as well as to deeper memory
hierarchies on these systems. In this work, we discuss challenges that arise in
in situ code coupling on these heterogeneous architectures. In particular, we
present data and execution model extensions to the SENSEI in situ framework
that are targeted at the effective use of systems with heterogeneous
architectures. We then use these new data and execution model extensions to
investigate several in situ placement and execution configurations and to
analyze the impact these choices have on overall performance.
