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Related Experiment Video

Updated: Mar 18, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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CudaChain: an alternative algorithm for finding 2D convex hulls on the GPU.

Gang Mei1

  • 1School of Engineering and Technology, China University of Geosciences, No. 29 Xueyuan Road, Beijing, 100083 China ; Institute of Earth and Environmental Science, University of Freiburg, Albertstr. 23B, 79104 Freiburg im Breisgau, Germany.

Springerplus
|June 29, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces CudaChain, a GPU-accelerated convex hull algorithm, and a novel Sorting-based Preprocessing Approach (SPA). CudaChain achieves significant speedups, offering an efficient solution for large planar point sets.

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Area of Science:

  • Computational Geometry
  • Computer Science
  • Algorithm Design

Background:

  • Convex hull computation is a fundamental problem in computational geometry.
  • Existing algorithms may face performance limitations with large datasets.
  • Efficient algorithms are crucial for applications in graphics, GIS, and machine learning.

Purpose of the Study:

  • To present a novel GPU-accelerated convex hull algorithm (CudaChain).
  • To introduce a Sorting-based Preprocessing Approach (SPA) for efficient point set processing.
  • To demonstrate the performance improvements of CudaChain over existing methods.

Main Methods:

  • Developed CudaChain, a two-stage GPU-accelerated algorithm.
  • Implemented a novel Sorting-based Preprocessing Approach (SPA) for discarding interior points.
  • Utilized the Thrust library for parallel sorting, reduction, and partitioning.
  • Combined GPU preprocessing with CPU finalization for convex hull calculation.

Main Results:

  • The Sorting-based Preprocessing Approach (SPA) effectively identifies and removes interior points.
  • CudaChain demonstrates 5x-6x speedups compared to the Qhull implementation for 20 million points.
  • The algorithm efficiently handles large planar point sets.

Conclusions:

  • CudaChain offers a highly efficient and scalable solution for computing convex hulls.
  • The Sorting-based Preprocessing Approach (SPA) significantly enhances preprocessing efficiency.
  • GPU acceleration is a viable strategy for accelerating convex hull algorithms.