Burlen Loring Lawrence Berkeley National Lab, E. Wes Bethel Lawrence Berkeley National Lab San Francisco State University, Gunther H. Weber Lawrence Berkeley National Lab, Michael W. Mahoney Lawrence Berkeley National Lab International Computer Science Institute University of California at Berkeley University of California at Berkeley

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.