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Towards utilizing GPUs in information visualization: a model and implementation of image-space operations.

Bryan McDonnel1, Niklas Elmqvist

  • 1Purdue University, West Lafayette, IN, USA. bmcdonne@purdue.edu

IEEE Transactions on Visualization and Computer Graphics
|October 17, 2009
PubMed
Summary

This paper bridges the gap between high-level data types and low-level GPU shaders for information visualization. It introduces an image-space pipeline refinement and a visual programming tool for shader construction.

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Area of Science:

  • Computer Science
  • Information Visualization
  • Computer Graphics

Background:

  • Modern Graphics Processing Units (GPUs) offer significant computational power for information visualization.
  • Current information visualization applications underutilize GPU capabilities due to a mismatch with GPU shader languages.
  • This mismatch stems from the difference between abstract data types and the low-level floating-point model of GPU shaders.

Purpose of the Study:

  • To propose a modified information visualization pipeline suitable for GPU shader implementation.
  • To address the underutilization of programmable GPUs in information visualization research.
  • To facilitate the creation of advanced visualization techniques leveraging GPU acceleration.

Main Methods:

  • A refinement of the traditional information visualization pipeline is presented, incorporating a final image-space sampling step.
  • Multivariate data is sampled at the resolution of the current view within this image-space step.
  • A visual programming environment with a drag-and-drop interface is developed for constructing visualization shaders.

Main Results:

  • The proposed pipeline refinement enables efficient implementation using GPU shaders.
  • The visual programming environment simplifies the creation and deployment of custom visualization shaders.
  • Demonstrations show the application of these shaders to well-known information visualization techniques.

Conclusions:

  • The developed approach effectively bridges the gap between abstract data types and GPU shader capabilities.
  • This work enhances the potential of GPUs for high-performance and visually flexible information visualization.
  • The introduced methods and tools empower researchers to create novel and efficient visualization applications.