Visualization at exascale: Making it all work with VTK-m

Moreland, Kenneth; Athawale, Tushar M.; Bolea, Vicente; Bolstad, Mark; Brugger, Eric; Childs, Hank; Huebl, Axel; Lo, Li-Ta; Geveci, Berk; Marsaglia, Nicole; Philip, Sujin; Pugmire, David; Rizzi, Silvio; Wang, Zhe; Yenpure, Abhishek

doi:10.1177/10943420241270969

Visualization at exascale: Making it all work with VTK-m

Journal Article · Wed Aug 14 00:00:00 EDT 2024 · International Journal of High Performance Computing Applications

DOI:https://doi.org/10.1177/10943420241270969· OSTI ID:2429459

^[1]; Athawale, Tushar M. ^[1]; ^[2]; Bolstad, Mark ^[3]; Brugger, Eric ^[4]; Childs, Hank ^[5]; ^[6]; Lo, Li-Ta ^[7]; Geveci, Berk ^[2]; Marsaglia, Nicole ^[4]; Philip, Sujin ^[2]; Pugmire, David ^[1]; Rizzi, Silvio ^[8]; Wang, Zhe ^[1]; Yenpure, Abhishek ^[2]

Oak Ridge National Laboratory, Computer Science and Mathematics, Oak Ridge, TN, USA
Kitware, Inc, Scientific Computing, Clifton Park, NY, USA
Sandia National Laboratories, Computing Research, Albuquerque, NM, USA
Lawrence Livermore National Laboratory, Computing, Livermore, CA, USA
University of Oregon, Computer and Information Science, Eugene, OR, USA
Lawrence Berkeley National Laboratory, Physical Sciences, Berkeley, CA, USA
Los Alamos National Laboratory, Computer, Computational, and Statistical Sciences, Los Alamos, NM, USA
Argonne National Laboratory, Argonne Leadership Computing Facility, Lemont, IL, USA

The VTK-m software library enables scientific visualization on exascale-class supercomputers. Exascale machines are particularly challenging for software development in part because they use GPU accelerators to provide the vast majority of their computational throughput. Algorithmic designs for GPUs and GPU-centric computing often deviate from those that worked well on previous generations of high-performance computers that relied on traditional CPUs. Fortunately, VTK-m provides scientific visualization algorithms for GPUs and other accelerators. VTK-m also provides a framework that simplifies the implementation of new algorithms and adds a porting layer to work across multiple processor types. This paper describes the main challenges encountered when making scientific visualization available at exascale. We document the surprises and obstacles faced when moving from pre-exascale platforms to the final exascale designs and the performance on those systems including scaling studies on Frontier, an exascale machine with over 37,000 AMD GPUs. We also report on the integration of VTK-m with other exascale software technologies. Finally, we show how VTK-m helps scientific discovery for applications such as fusion and particle acceleration that leverage an exascale supercomputer.

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Sponsoring Organization:: USDOE

OSTI ID:: 2429459

Alternate ID(s):: OSTI ID: 2564977
OSTI ID: 2585328
OSTI ID: 2455093

Journal Information:: International Journal of High Performance Computing Applications, Journal Name: International Journal of High Performance Computing Applications; ISSN 1094-3420

Publisher:: SAGE PublicationsCopyright Statement

Country of Publication:: United States

Language:: English

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Visualization at exascale: Making it all work with VTK-m

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