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LA-UR- 13-21083 Approved for public release; distribution is unlimited. Title: PISTON: An SDAV Framework for Portable High-Performance Data-Parallel Visualization and Analysis Operators Author(s): Christopher Sewell, Li-ta Lo, and James


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Form 836 (7/06)

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Approved for public release; distribution is unlimited. Los Alamos National Laboratory, an affirmative action/equal opportunity employer, is operated by the Los Alamos National Security, LLC for the National Nuclear Security Administration of the U.S. Department of Energy under contract DE-AC52-06NA25396. By acceptance

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PISTON: An SDAV Framework for Portable High-Performance Data-Parallel Visualization and Analysis Operators Christopher Sewell, Li-ta Lo, and James Ahrens SDAV (Scalable Data Management, Analysis, and Visualization) Sci-Dac All-Hands Meeting, Burlingame, CA, February 20-22, 2013

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Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA

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Abstract for “PISTON: An SDAV Framework for Portable High-Performance Data-Parallel Visualization and Analysis Operators” This presentation describes the overall goal of PISTON (to provide portability and performance for visualization and analysis operators

  • n current and next-generation supercomputers), and summarizes the

work on PISTON in relation to the SDAV (The SciDac Institute of Scalable Data Management, Analysis, and Visualization) Milestones. Specifically, it presents work related to general PISTON algorithm and infrastructure development; the halo finder operator; PISTON integration into VTK and ParaView; VPIC in-situ PISTON pipelines; and publications, presentations, and tutorials.

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Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA

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PISTON: An SDAV Framework for Portable High- Performance Data-Parallel Visualization and Analysis Operators

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Chris Sewell, Li-Ta Lo, and James Ahrens Los Alamos National Laboratory

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Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA

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High-Level Summary of PISTON

  • Goal: Portability and performance for visualization and analysis
  • perators on current and next-generation supercomputers, taking

advantage of available parallelism on each architecture

  • Main idea: Write operators using only data-parallel primitives (scan,

reduce, etc.)

  • Requires architecture-specific optimizations for only for the small set of

primitives

  • PISTON is built on top of NVIDIA’s Thrust Library

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Data-Parallel Primitives and Algorithms

Examples of data-parallel primitives Isosurface algorithm constructed using

  • nly data-parallel primitives

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SDAV Visualization Milestones Involving PISTON

  • Recap of Milestones
  • 3.3.1: VisIt and ParaView & Kitware (Kitware, LANL, LBNL, LLNL, ORNL, SNL):

Y1 (March 2013): Enhance VisIt and ParaView to leverage multiple cores within a single MPI task

  • 3.3.2: VTK-m Framework (ANL, Kitware, LANL, LBNL, LLNL, ORNL, SNL):

Y1 (March 2013): Enhancements to existing multi/many-core tech. in anticipation of in situ analysis use cases with LCF codes

Y2 (March 2014): Deployment and evaluation of existing technologies in prototype form

  • Projects
  • General PISTON algorithm and infrastructure development (3.3.2 Y1 & Y2)
  • Halo finder (3.3.2 Y1 & Y2)
  • PISTON integration into VTK and ParaView (3.3.1 Y1, 3.3.2 Y2)
  • VPIC in-situ PISTON pipelines (3.3.2 Y2)
  • Publications, presentations, panels, and tutorials (3.3.2 Y2)

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General PISTON Development: Highlights

  • We have run visualization algorithms on GPUs and on multi-core CPUs using the exact same
  • perator code by compiling to CUDA and OpenMP backends
  • Algorithms: isosurface, threshold, cut surface, glyph, “boid” simulation, KD-tree, simple

rendering (rasterizer and ray caster)

  • Data structures supported: regular grids, curvilinear coordinates, unstructured tetrahedra
  • Backends: prototype OpenCL backend, improvements to OpenMP backend

Isosurface and cut surface on a Rayleigh-Taylor Instability data set Marching tetrahedra isosurface computed

  • n an AMR mesh

Isosurfaces computed

  • n a curvilinear grid

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Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA

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General PISTON Development: Current Work

  • Distributed implementations of data-parallel primitives, and higher-level

algorithms using them on large, distributed data sets

  • Companion LDRD “PINION” project: Xeon Phi optimized backend; new data

structures for simulation meshes; data-parallel implementation of physics simulations

Isosurface computation distributed across MPI ranks using VTK, with each processor using PISTON locally Isosurface computed using data-parallel primitives across MPI ranks Overview of PINION LDRD project, which focuses on developing physics-based data models and operators, and implementing data- parallel primitives optimized for emerging architectures

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Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA

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Halo Finder

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  • Implemented by Wathsala Widanagamaachchi, a Ph.D. student at Univ. of Utah with Valerio Pascucci
  • Implemented using PISTON, including our data-parallel KD-tree construction algorithm
  • Bottom-up traversal of the KD-tree, comparing all pairs of points within the linking length of the split value
  • Wathsala, Valerio, Timo, and LANL are developing a parallel algorithm to construct merge trees for halos
  • Potential eventual integration with LCF HACC code

KD-tree construction and traversal for a sample set of points Input data (left) and output halos (right) for a given linking length

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Integration with VTK and ParaView

  • Filters that use PISTON data types and algorithms are integrated into VTK and can be

used in ParaView with the PISTON plug-in

  • Utility filters interconvert between standard VTK data format and PISTON data format

(thrust device vectors), transferring data between CPU and GPU

  • Supports interop for on-card rendering

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Software design of VTK integration Isosurface generated using ParaView PISTON plug-in

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PISTON In-Situ with VPIC

  • Our in-situ adapter for VPIC (Vector Particle in Cell), an LCF kinetic plasma simulation code,

makes use of PISTON via the ParaView Co-Processing Library (Catalyst)

  • Hard-coded vtkPistonContour pipeline executed on HPC cluster (moonlight)
  • See John Patchett’s talk for more details about our in-situ work, including other codes and
  • ther pipelines unrelated to PISTON

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Contours of the Hhydro charge density produced using PISTON in-situ with VPIC Contours of the Hhydro charge density produced using ParaView in post-processing Surface line integral contour produced in-situ with VPIC

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Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA

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Tutorials, Presentations, and Publications

  • Publications
  • Christopher Sewell, Jeremy Meredith, Kenneth Moreland, Tom Peterka, Dave DeMarle, Li-ta Lo, James Ahrens, Robert Maynard, and Berk
  • Geveci. "The SDAV Software Frameworks for Visualization and Analysis on Next-Generation Multi-Core and Many-Core Architectures".

Proceedings of the Workshop on Ultrascale Visualization at the International Conference for High-Performance Computing, Networking, Storage, and Analysis, November 2012.

  • Li-ta Lo, Christopher Sewell, and James Ahrens. "PISTON: A Portable Cross-Platform Framework for Data-Parallel Visualization Operators".

Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization, May 2012.

  • Presentations
  • Visualization Frameworks for Multi-Core and Many-Core Architectures Panel at Supercomputing, November2012, Salt Lake City, UT
  • Ultrascale Visualization Workshop at Supercomputing, November 2012, Salt Lake City, UT
  • Accelerated High Performance Computing Symposium at the GPU Technology Conference, May 2012, San Jose, CA
  • Eurographics Symposium on Parallel Graphics and Visualization, May 2012, Cagliari, Italy
  • Department of Energy Computer Graphics Forum, April 2012, Albuquerque, NM
  • Ultrascale Visualization Workshop at Supercomputing, November 2011, Seattle, WA
  • Tutorials
  • Many-Core Libraries Tutorial and Code Sprint, September 2012, Clifton Park, NY
  • PISTON Tutorial, October 2012, Los Alamos, NM

Listings in bold were activities also involving

  • ther VTK-m frameworks

(EAVL/Dax/DIY)

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Acknowledgments and Resources

  • PISTON project homepage: http://viz.lanl.gov/projects/PISTON.html
  • The SciDAC Institute of Scalable Data Management, Analysis and

Visualization (SDAV) is funded by the DOE Office of Science through the Office of Advanced Scientific Computing Research.

  • SciDAC Institute Director: Arie Shoshani
  • Visualization Project Chairs: James Ahrens, Wes Bethel
  • http://sdav-scidac.org/
  • Related PISTON projects also funded by ASC Program, ASCR, LANL LDRD

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