The VTK-m Users' Guide (V.1.9)

Technical Report · Wed Oct 12 04:00:00 EDT 2022

DOI:https://doi.org/10.2172/1895214· OSTI ID:1895214

Moreland, Kenneth ^[1]; Bolea, Vicente ^[1]; Childs, Hank ^[1]; Davis, Nickolas ^[1]; Kim, Mark ^[1]; Kress, James ^[1]; Letter, Matthew ^[1]; Lo, Li-Ta ^[1]; Maynard, Robert ^[1]; Philip, Sujin ^[1]; Pugmire, David ^[1]; Thompson, Nick ^[1]; Vacanti, Allison ^[1]; Yenpure, Abhishek ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

High-performance computing relies on ever finer threading. Advances in processor technology include ever greater numbers of cores, hyperthreading, accelerators with integrated blocks of cores, and special vectorized instructions, all of which require more software parallelism to achieve peak performance. Traditional visualization solutions cannot support this extreme level of concurrency. Extreme scale systems require a new programming model and a fundamental change in how we design algorithms. To address these issues we created VTK-m: the visualization toolkit for multi-/many-core architectures. VTK-m supports a number of algorithms and the ability to design further algorithms through a top-down design with an emphasis on extreme parallelism. VTK-m also provides support for finding and building links across topologies, making it possible to perform operations that determine manifold surfaces, interpolate generated values, and find adjacencies. Although VTK-m provides a simplified high-level interface for programming, its template-based code removes the overhead of abstraction. VTK-m simplifies the development of parallel scientific visualization algorithms by providing a framework of supporting functionality that allows developers to focus on visualization operations. Consider the listings in Figure 1.1 that compares the size of the implementation for the Marching Cubes algorithm in VTK-m with the equivalent reference implementation in the CUDA software development kit. Because VTK-m internally manages the parallel distribution of work and data, the VTK-m implementation is shorter and easier to maintain. Additionally, VTK-m provides data abstractions not provided by other libraries that make code written in VTK-m more versatile.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC05-00OR22725; NA0003525
• OSTI ID: 1895214
• Report Number(s): ORNL/TM-2022/2744
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING