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Using Umpire In-Situ for Improved Memory Performance
DescriptionBecause memory is a highly constrained resource, Umpire, a data and memory management API, was created at Lawrence Livermore National Laboratory (LLNL). Umpire provides memory pools which enable less expensive ways to allocate very large amounts of memory in HPC environments. Additionally, memory pools can be used when many small allocations are needed to avoid expensive calls to the underlying device-specific API. In-situ visualization is inherently
resource constrained, making Umpire’s memory management API a valuable tool for improving performance. Umpire is used in many simulation codes at LLNL that also rely on cutting-edge in-situ visualization libraries. This lightning talk discusses Umpire's advantages and use cases, including some examples of in-situ visualization applications which rely on Umpire to improve memory performance.
resource constrained, making Umpire’s memory management API a valuable tool for improving performance. Umpire is used in many simulation codes at LLNL that also rely on cutting-edge in-situ visualization libraries. This lightning talk discusses Umpire's advantages and use cases, including some examples of in-situ visualization applications which rely on Umpire to improve memory performance.
Event Type
Workshop
TimeMonday, 13 November 202311:40am - 11:50am MST
Location506
Data Analysis, Visualization, and Storage
Large Scale Systems
Performance Measurement, Modeling, and Tools
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