If you strayed into the corridors of Kitware or one of the ParaView developer meetings, you could not miss hearing talk about ParaView 4.0. As the team actively discusses new features and improvements we want to include in this major release for ParaView, I couldn’t help but remember some of the early meetings we had when we were planning for ParaView 3.0. There were many improvements discussed with 3.0, but one of the major ones was the support for customization. Our goal was to make a framework that would allow the development of sophisticated, high-end parallel visualization applications based on the ParaView infrastructure as well as a plugin-based architecture allowing developer add new features and enhancements to ParaView with ease. Through various releases since, this infrastructure has undergone several evolutions, but the underpinnings remain the same. For the longest time, all this seemed hypothetical, until my recent visit to IEEE VisWeek.
I was at the first ever IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV) held in conjunction with VisWeek 2011. The symposium focused on “bringing together domain scientists, data analytics and visualization researchers, and users, and fostering the needed exchange to develop the next-generation data-intensive analysis and visualization technology”. In a day that was very well attended until the fat lady sang — so to speak — over 14 papers were presented ranging from novel approaches to dealing with visualization challenges for new architectures to understanding how architectural decisions affect visualization and analysis. It was expected to be an interesting day; what was a pleasant surprise, for me at the least, was that several of the presenters mentioned customizations for ParaView! Some demonstrated their capabilities by integrating code into ParaView, a couple discussed how ParaView can be extended using proposed enhancements to a greater benefit. In this article, I give a quick overview of the papers that refer ParaView. Basically, a summary of the cool things the community is doing with ParaView.
Joe Insley, in his paper “Visualizing Multiscale, Multiphysics Simulation: Brain Blood Flow”, discussed how he and his team developed visualization tools for data from coupled continuum-atomistic simulations. They developed a ParaView reader plugin for processing macro-scale continuum data computed by a high-order spectral element solver. The reader plugin will be distributed with the official 3.12 ParaView binaries.
Venkat Vishwanath presented their work on GLEAN in “Toward Simulation-Time Data Analysis and I/O Acceleration on Leadership-class Systems”. GLEAN is a “flexible framework for data-analysis and I/O acceleration at extreme scale”. In a nutshell, GLEAN provides extensions to the I/O libraries that make it possible to leverage the network topology and other parameters for efficient I/O. At the same time, GLEAN enables in-situ analysis as the data is being streamed out for I/O. They demonstrated GLEAN’s co-visualization capabilities by connecting PHASTA (a CFD code) to ParaView through GLEAN.
As data sizes keep growing, fast, interactive visualization and analysis becomes a challenge. John Patchett, presenting the paper by Jon Woodring et. al. titled “Revisiting Wavelet Compression for Large-Scale Climate Data using JPEG 2000 and Ensuring Data Precision”, argued that the majority of these bottlenecks are due to data movement and associated bandwidth limitations. Using wavelet compression in JPEG 2000, John presented a mechanism to achieve improved data-transfer times by sacrificing data quality to varying extents. The team, in collaboration with Kitware, developed a working prototype within ParaView that uses reader plugins and new streaming views and enhancements to the VTK pipeline.
Nathan Fabian’s “The ParaView Coprocessing Library: A Scalable, General Purpose In Situ Visualization Library”, another paper involving contributing from members of the scientific visualization team at Kitware, presented ParaView’s coprocessing library. It is a framework for in situ visualization and analysis. The coprocessing algorithms can be directly linked and executed within the simulation code. The paper presented results from integrating the ParaView coprocessing library into various simulation codes such as PHASTA, CTH, S3D.
Oddly enough, it seemed like I was one of the few presenters at the symposium not talking about ParaView at all (I was there presenting the Dax Toolkit)! I suppose after talking ParaView day in and day out, it’s refreshing to shut up and listen to what others have to say about ParaView.