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The SENSEI Generic In Situ Interface: Tool and Processing Portability at Scale [Book Chapter]

Book ·
 [1];  [1];  [2];  [1];  [3];  [4];  [4];  [1];  [5];  [2];  [5];  [4];  [2];  [1];  [3];  [6];  [1]
  1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  2. Kitware, Inc., Clifton Part, NY (United States)
  3. Intelligent Light, Rutherford, NJ (United States)
  4. Argonne National Laboratory (ANL), Argonne, IL (United States)
  5. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  6. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Kitware, Inc., Clifton Part, NY (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Intelligent Light, Rutherford, NJ (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
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One key challenge when doing in situ processing is the investment required to add code to numerical simulations needed to take advantage of in situ processing. Such instrumentation code is often specialized, and tailored to a specific in situ method or infrastructure. Then, if a simulation wants to use other in situ tools, each of which has its own bespoke API [4], then the simulation code team will quickly become overwhelmed with having a different set of instrumentation APIs, one per in situ tool or method. In an ideal situation, such instrumentation need happen only once, and then the instrumentation API provides access to a large diversity of tools. In this way, a data producer’s instrumentation need not be modified if the user desires to take advantage of a different set of in situ tools. The SENSEI generic in situ interface addresses this challenge, which means that SENSEI-instrumented codes enjoy the benefit of being able to use a diversity of tools at scale, tools that include Libsim, Catalyst, Ascent, as well as user-defined methods written in C++ or Python. SENSEI has been shown to scale to greater than 1M-way concurrency on HPC platforms, and provides support for a rich and diverse collection of common scientific data models. Furthermore, this chapter presents the key design challenges that enable tool and processing portability at scale, some performance analysis, and example science applications of the methods.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
DOE Contract Number:
AC02-05CH11231; AC02-06CH11357
OSTI ID:
2448487
Country of Publication:
United States
Language:
English

References (11)

Fast Mesh Validation in Combustion Simulations through In-Situ Visualization text January 2019
Python-based in situ analysis and visualization conference November 2018
VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures journal May 2016
A terminology for in situ visualization and analysis systems journal August 2020
Visualization and Data Analytics Challenges of Large-Scale High-Fidelity Numerical Simulations of Wind Energy Applications conference January 2018
Comparing the Efficiency of In Situ Visualization Paradigms at Scale book January 2019
The SENSEI Generic In Situ Interface
  • Ayachit, Utkarsh; Whitlock, Brad; Wolf, Matthew
  • 2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV) https://doi.org/10.1109/ISAV.2016.013
conference November 2016
Local adaptive mesh refinement for shock hydrodynamics journal May 1989
Performance Analysis, Design Considerations, and Applications of Extreme-Scale In Situ Infrastructures
  • Ayachit, Utkarsh; Bauer, Andrew; Duque, Earl P. N.
  • SC16: International Conference for High Performance Computing, Networking, Storage and Analysis https://doi.org/10.1109/SC.2016.78
conference November 2016
On the Strong Scaling of the Spectral Element Solver Nek5000 on Petascale Systems conference January 2016
Master of Puppets: Cooperative Multitasking for In Situ Processing conference January 2016

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