ABSTRACT
This paper introduces ALPINE, a flyweight in situ infrastructure. The infrastructure is designed for leading-edge supercomputers, and has support for both distributed-memory and shared-memory parallelism. It can take advantage of computing power on both conventional CPU architectures and on many-core architectures such as NVIDIA GPUs or the Intel Xeon Phi. Further, it has a flexible design that supports for integration of new visualization and analysis routines and libraries. The paper describes ALPINE's interface choices and architecture, and also reports on initial experiments performed using the infrastructure.
- 2017. MFEM: Modular finite element methods. (2017). http://mfem.orgGoogle Scholar
- James Ahrens, Berk Geveci, and Charles Law. 2005. Paraview: An end-user tool for large data visualization. The Visualization Handbook 717 (2005).Google Scholar
- James Ahrens, Sébastien Jourdain, Patrick O'Leary, John Patchett, David H. Rogers, and Mark Petersen. 2014. An Image-based Approach to Extreme Scale in Situ Visualization and Analysis. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '14). IEEE Press, Piscataway, NJ, USA, 424--434. https://doi.org/10.1109/SC.2014.40 Google ScholarDigital Library
- Utkarsh Ayachit et al. 2016. The SENSEI Generic in Situ Interface. In Proceedings of the 2Nd Workshop on In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization (ISAV '16). 40--44. Google ScholarCross Ref
- Utkarsh Ayachit, Andrew Bauer, Berk Geveci, Patrick O'Leary, Kenneth Moreland, Nathan Fabian, and Jeffrey Mauldin. 2015. Paraview catalyst: Enabling in situ data analysis and visualization. In Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization. ACM, 25--29. Google ScholarDigital Library
- Andrew C. Bauer, Hasan Abbasi, James Ahrens, Hank Childs, Berk Geveci, Scott Klasky, Kenneth Moreland, Patrick O'Leary, Venkatram Vishwanath, Brad Whitlock, and E. Wes Bethel. 2016. In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms, a State-of-the-art (STAR) Report. Computer Graphics Forum, Proceedings of Eurovis 2016 35, 3 (June 2016). LBNL-1005709.Google Scholar
- Hank Childs et al. 2012. VisIt: An End-User Tool For Visualizing and Analyzing Very Large Data. In High Performance Visualization---Enabling Extreme-Scale Scientific Insight. CRC Press/Francis--Taylor Group, 357--372.Google Scholar
- William Gropp, Ewing Lusk, Nathan Doss, and Anthony Skjellum. 1996. A high-performance, portable implementation of the MPI message passing interface standard. Parallel computing 22, 6 (1996), 789--828. Google ScholarDigital Library
- C. Harrison, P. Navràtil, M. Moussalem, M. Jiang, and H. Childs. 2012. Efficient Dynamic Derived Field Generation on Many-Core Architectures Using Python. In Proceedings of the 2012 Workshop on Python for High Performance and Scientific Computing (PyHPC 2012). 583--592. https://doi.org/10.1109/SC.Companion.2012.82Google Scholar
- Lawrence Livermore National Laboratory. 2017. Conduit: Simplified Data Exchange for HPC Simulations. (2017). https://software.llnl.gov/conduit/Google Scholar
- Lawrence Livermore National Laboratory. 2017. Conduit: Simplified Data Exchange for HPC Simulations - Conduit Blueprint. (2017). https://software.llnl.gov/conduit/blueprint.htmlGoogle Scholar
- Matthew Larsen, Eric Brugger, Hank Childs, Jim Eliot, Kevin Griffin, and Cyrus Harrison. 2015. Strawman: A Batch In Situ Visualization and Analysis Infrastructure for Multi-Physics Simulation Codes. In Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV2015). ACM, New York, NY, USA, 30--35. https://doi.org/10.1145/2828612.2828625 Google ScholarDigital Library
- Jay F. Lofstead, Scott Klasky, Karsten Schwan, Norbert Podhorszki, and Chen Jin. 2008. Flexible IO and Integration for Scientific Codes Through the Adaptable IO System (ADIOS). In Proceedings of the 6th International Workshop on Challenges of Large Applications in Distributed Environments (CLADE '08). ACM, New York, NY, USA, 15--24. https://doi.org/10.1145/1383529.1383533 Google ScholarDigital Library
- Jeremy S. Meredith, Sean Ahern, Dave Pugmire, and Robert Sisneros. 2012. EAVL: The Extreme-scale Analysis and Visualization Library. In Eurographics Symposium on Parallel Graphics and Visualization. The Eurographics Association.Google Scholar
- Paul Messina. 2017. The Exascale Computing Project. Computing in Science & Engineering 19, 3 (2017), 63--67. Google ScholarCross Ref
- Kenneth Moreland, Christopher Sewell, William Usher, Lita Lo, Jeremy Meredith, David Pugmire, James Kress, Hendrik Schroots, Kwan-Liu Ma, Hank Childs, Matthew Larsen, Chun-Ming Chen, Robert Maynard, and Berk Geveci. 2016. VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures. IEEE Computer Graphics and Applications (CG&A) 36, 3 (May/June 2016), 48--58.Google ScholarCross Ref
- Tom Peterka, Robert Ross, Attila Gyulassy, Valerio Pascucci, Wesley Kendall, Han-Wei Shen, Teng-Yok Lee, and Abon Chaudhuri. 2011. Scalable parallel building blocks for custom data analysis. In Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on. IEEE, 105--112.Google ScholarCross Ref
- Tom Peterka, Robert Ross, Wesley Kendall, Attila Gyulassy, Valerio Pascucci, Han-Wei Shen, Teng-Yok Lee, and Abon Chaudhuri. 2011. Scalable Parallel Building Blocks for Custom Data Analysis. In Proceedings of Large Data Analysis and Visualization Symposium LDAV'11. Providence, RI. Google ScholarCross Ref
- R Core Team. 2000. R language definition. Vienna, Austria: R foundation for statistical computing (2000).Google Scholar
- Brad Whitlock, Jean M. Favre, and Jeremy S. Meredith. 2011. Parallel in Situ Coupling of Simulation with a Fully Featured Visualization System. In Proceedings of the 11th Eurographics Conference on Parallel Graphics and Visualization (EGPGV). 101--109.Google Scholar
Index Terms
- The ALPINE In Situ Infrastructure: Ascending from the Ashes of Strawman
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