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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Related Experiment Video

Updated: Jan 4, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Scalable Contour Tree Computation by Data Parallel Peak Pruning.

Hamish A Carr, Gunther H Weber, Christopher M Sewell

    IEEE Transactions on Visualization and Computer Graphics
    |November 6, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the first shared Symmetric Multiprocessing (SMP) algorithm for parallel contour tree computation. This novel approach significantly enhances the scalability and speed of analyzing large datasets, outperforming serial methods.

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    Area of Science:

    • High-performance computing
    • Data analysis and visualization
    • Scientific computing

    Background:

    • Exascale data growth necessitates automated analysis and in situ processing.
    • High-performance computing architectures demand parallelizable algorithms.
    • Contour trees are vital for feature identification but traditional algorithms are serial and limit scalability.

    Purpose of the Study:

    • To develop an efficient, scalable, and formally guaranteed parallel algorithm for contour tree computation.
    • To address the limitations of existing serial algorithms in the context of modern computing architectures.
    • To improve the practical performance of contour tree computation on multicore and distributed systems.

    Main Methods:

    • Development of the first shared Symmetric Multiprocessing (SMP) algorithm for fully parallel contour tree computation.
    • Formal performance guarantees of O(lg V lg t) parallel steps and O(V lg V) work.
    • Implementation and testing on CPU (using TBB) and GPU (using Thrust).

    Main Results:

    • Achieved over 30x parallel speedup on CPU and GPU.
    • Demonstrated up to 70x speedup compared to the serial sweep and merge algorithm.
    • Validated formal performance guarantees in practical implementations.

    Conclusions:

    • The presented shared SMP algorithm offers a significant advancement in parallel contour tree computation.
    • This parallel approach effectively addresses the scalability challenges posed by large datasets and modern HPC systems.
    • The algorithm provides a robust and efficient solution for in situ data analysis and feature identification.