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Extensions to the SENSEI In situ Framework for Heterogeneous Architectures
DescriptionThe presence of GPUs and accelerators in recent super computing
systems, so called heterogeneous architectures, has lead to increased
complexity in execution environments and programming models
as well as deeper memory hierarchies on these systems. In this
work we discuss challenges that arise in in situ code coupling on
heterogeneous architectures. We present data and execution model
extensions to the SENSEI in situ framework targeted at effective use
of systems with heterogeneous architectures. We use the new data
and execution model extensions in SENSEI to investigate a number
of in situ placement and execution configurations and analyze the
impact these choices have on overall performance.
systems, so called heterogeneous architectures, has lead to increased
complexity in execution environments and programming models
as well as deeper memory hierarchies on these systems. In this
work we discuss challenges that arise in in situ code coupling on
heterogeneous architectures. We present data and execution model
extensions to the SENSEI in situ framework targeted at effective use
of systems with heterogeneous architectures. We use the new data
and execution model extensions in SENSEI to investigate a number
of in situ placement and execution configurations and analyze the
impact these choices have on overall performance.
Event Type
Workshop
TimeMonday, 13 November 202310:30am - 10:50am MST
Location506
Data Analysis, Visualization, and Storage
Large Scale Systems
Performance Measurement, Modeling, and Tools
W