What visualization system designs are most effective for in situ processing?

In situ processing creates several new challenges that will require fresh approaches for visualization systems. One challenge is that increased pressure on resources -- an in situ system's use of memory, compute power, energy, or the interconnect network -- can affect the simulation. Another challenge is in integration. With post hoc processing, integration occurred via files, i.e., a simulation code wrote files and a visualization problem read files. In some in situ settings, integrations require linking visualization algorithms into the simulation code, requiring each simulation code to be extended to invoke in situ APIs and exchange data. Further, this approach can lead to practical issues regarding complex compilation processes and issues stemming from large binary size.

This page describes three research thrusts on visualization systems:

PaViz

The PaViz system was a major part of Stephanie Labasan's dissertation research, which focused on power/performance tradeoffs for scientific visualization workloads on supercomputers. Her research took two distinct phases. In the first phase, she established that the data-intensive nature of visualization workloads can create useful propositions, e.g., for 10% slower performance, you can save 40% on energy. She did this by first studying one algorithm in depth and then performing a more comprehensive study on a wider class of algorithms. In the second phase, she considered overprovisioned supercomputers (where there are more compute resources than power) and designed approaches in the PaViz system which direct power to where it will improve overall performance the most. Her approach differed from previous works not only in that it considered visualization, but also in that it used prediction to direct power, as opposed to the standard practice of adaption. Stephanie then performed a thorough study comparing prediction and adaption for rendering workloads, finding that prediction works best.

Ascent

The Ascent library has a special focus on "flyweight in situ," meaning small API, small binary size, small execution overhead, and small memory footprint. CDUX alumnus Matt Larsen is the lead developer of Ascent, and the product began as during Matt's time as a Ph.D. student as Strawman, a "visualization mini-app." Ascent is central part of the Department of Energy’s strategy for in situ visualization at the exascale, and has been integrated with multiple simulation codes. Our students also often perform their research within Ascent, and run with real simulation codes on DOE supercomputers. This includes the research on Lagrangian flow and wavelets and the research on estimating and optimizing in situ costs. Ascent also heavily uses VTK-m, incorporating CDUX results on portable performance. Finally, CDUX members have researched approaches for "triggers" within Ascent, which are mechanisms for adapting when visualization is performed based on simulation properties. Specifically, Matt Larsen, Nicole Marsaglia, and others proposed a system for in situ triggers that was awarded Best Paper at ISAV18. More recently, Yuya Kawakami led an effort that used this system to begin benchmarking trigger efficacy.

VisIt

VisIt was originally developed by the Department of Energy (DOE) Advanced Simulation and Computing Initiative (ASCI) to visualize and analyze the results of terascale simulations. Hank Childs was a founding member of the VisIt development team, and he served as the project architect 2000-2013. VisIt was designed with a high degree of modularity to support rapid deployment of new visualization technology. This includes a plugin architecture for custom readers, data operators and plots as well as the ability to support multiple different user interfaces. Following a prototyping effort in the summer of 2000, an initial version of VisIt was developed and released in the fall of 2002. Since then, over 100 database readers, 60 operators and 20 plots have been added to the open source code. In addition, commercial, government and academic organizations in the US, Europe and elsewhere have developed and maintained proprietary plugins and user interfaces for their own needs. Although the primary driving force behind the original development of VisIt was for visualizing ASCI terascale data, VisIt has also proven to be well suited for visualizing smaller scale data from simulations on desktop systems. With respect to in situ processing, Brad Whitlock used the VisIt source code to make "LibSim," which was an in situ version of VisIt. Finally, Hank's Ph.D. dissertation was built around the research he performed while helping to develop the tool, including a contract-based system to adapt which optimizations were applied based on the properties of a data flow network.

CDUX People

Stephanie Labasan
Ph.D. Student (alum)

Matt Larsen
Ph.D. Student (alum)

Yuya Kawakami
M.S. Student

Nicole Marsaglia
Ph.D. Candidate

Hank Childs
CDUX Director
Publications


Evaluating Adaptive and Predictive Power Management Strategies for Optimizing Visualization Performance on Supercomputers
Stephanie Brink, Matthew Larsen, Hank Childs, and Barry Rountree
Parallel Computing, July 2021

[PDF]     [BIB]

Power and Performance Tradeoffs for Visualization Algorithms
Stephanie Labasan, Matt Larsen, Hank Childs, and Barry Rountree
IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil, May 2019

[PDF]     [BIB]

PaViz: A Power-Adaptive Framework for Optimizing Visualization Performance
Stephanie Labasan, Matt Larsen, Hank Childs, and Barry Rountree
Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), Barcelona, Spain, June 2017

[PDF]     [BIB]

Exploring Tradeoffs Between Power and Performance for a Scientific Visualization Algorithm
Stephanie Labasan, Matthew Larsen, and Hank Childs
IEEE Symposium on Large Data Analysis and Visualization (LDAV), Chicago, IL, October 2015

[PDF]     [BIB]

The ALPINE In Situ Infrastructure: Ascending from the Ashes of Strawman
Matt Larsen, James Ahrens, Utkarsh Ayachit, Eric Brugger, Hank Childs, Berk Geveci, and Cyrus Harrison
ISAV 2017: In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization, Denver, CO, November 2017

[PDF]     [BIB]

A Flexible System For In Situ Triggers
Matt Larsen, Amy Woods, Nicole Marsaglia, Ayan Biswas, Soumya Dutta, Cyrus Harrison, and Hank Childs
ISAV 2018: In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization, Dallas, TX, November 2018
Best Paper

[PDF]     [BIB]

Benchmarking In Situ Triggers Via Reconstruction Error
Yuya Kawakami, Nicole Marsaglia, Matt Larsen, and Hank Childs
ISAV 2020: In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization, Atlanta GA, November 2020

[PDF]     [BIB]

Strawman - A Batch In Situ Visualization and Analysis Infrastructure for Multi-Physics Simulation Codes
Matthew Larsen, Eric Brugger, Hank Childs, Jim Eliot, Kevin Griffin, and Cyrus Harrison
SC15 Workshop on In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization (ISAV-15), Austin, TX, November 2015

[PDF]     [BIB]

VisIt: An End-User Tool For Visualizing and Analyzing Very Large Data
Hank Childs, Eric Brugger, Brad Whitlock, Jeremy Meredith, Sean Ahern, Kathleen Bonnell, Mark Miller, Gunther H. Weber, Cyrus Harrison, David Pugmire, Thomas Fogal, Christoph Garth, Allen Sanderson, E. Wes Bethel, Marc Durant, David Camp, Jean M. Favre, Oliver Ruebel, Paul Navratil, Matthew Wheeler, Paul Selby, and Fabien Vivodtzev
DOE SciDAC Conference, Denver, CO, July 2011

[PDF]     [BIB]

A Contract-Based System for Large Data Visualization
Hank Childs, Eric Brugger, Kathleen Bonnell, Jeremy Meredith, Mark Miller, Brad Whitlock, and Nelson Max
IEEE Visualization, Minneapolis, MN, October 2005

[PDF]     [BIB]