Pictures, in Parallel.(Technology Information)

Los Alamos researchers hope to bring the power of multiprocessing to scientific visualization

The mega advances that have defined computational processing developments over the past several years have enabled scientists to simulate extremely large, time-varying datasets in a matter of hours, versus the days and weeks that were required in years past. Today researchers are resolving models of real-world phenomena ranging from the Earth's climate and oceanic activity to accelerator-physics dynamics by running simulations in parallel on clusters of high-bandwidth supercomputers and PCs. Because of these capabilities, as well as significant algorithmic advances, the simulation models being crunched are able to achieve an unprecedented level of detail resolution.

The solutions are so large and complex, however, that similarly powerful computational resources are needed to visualize and analyze the resulting datasets. Unfortunately, general-purpose parallel visualization programs are neither easy to come by nor easy to implement. Typically, such programs require highly specified programming knowledge beyond the ken of the scientists running the simulations. In addition, when they are programmed, the parallel systems tend to be "one-offs," meaning they are developed to visualize a specific simulation but cannot be generalized to other applications.

In an effort to make parallel visualization accessible to a broader range of users, researchers at Los Alamos National Laboratory, Kitware Inc., and Argonne National Laboratory have built a scalable, full-function system on top of an existing, open-source toolkit that allows users to quickly write custom parallel visualization programs that they can then port across platforms without having to rewrite the code. Called the Parallel Visualization Toolkit, the new system uses existing software, including the open-source Visualization Toolkit (VTK) developed by researchers at Kitware and GE Corporate R&D Center, as well as OpenGL and Mesa APIs.

The researchers chose VTK as their starting point because the toolkit contains a broad array of visualization, graphics, and imaging algorithms and is portable to a variety of hardware platforms and operating systems. Their goal was to support most of the VTK functionality by offering parallel versions of the visualization algorithms. "Supporting a full range of parallel-visualization algorithms is critical to effectively processing large datasets, since the alternative--interspersing serial algorithms with parallel algorithms--can significantly degrade performance" says LANL researcher James Ahrens.

VTK's multi-platform support was also a deciding factor. "Portability is critical to users with access to heterogeneous platforms, because platform availability can change due to crashes, maintenance, purchases, and removal. If the user's visualization system is portable, then he or she can choose the best available platform, instead of being constrained only to the availability of a specific platform," says Ahrens. Thus, a fundamental objective was to create a system that would be portable between platforms with different operating systems and underlying hardware, and between shared and distributed memory multiprocessors. This was achieved with a programming mechanism through which the system automatically abstracts whether the process being implemented uses a distributed or shared-memory approach. Once this environment was established, the researchers abstracted the complex parallel computing details to simplify the creation of parallel visualization programs.

The resulting framework provides a reusable infrastructure for parallel and distributed visualization to solve terascale visualization programs. It does ...