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Massively Multithreaded Maxflow for Image Segmentation on the Cray XMT-2.

Shahid H Bokhari¹, Ümit V Çatalyürek², Metin N Gurcan³

¹Algopath LLC, 20660 Stevens Creek Blvd. #164, Cupertino, CA, 95014.

Concurrency and Computation : Practice & Experience

|January 20, 2015

Summary

This summary is machine-generated.

We optimized the Goldberg-Tarjan preflow-push algorithm for parallel image segmentation using the Cray XMT-2 supercomputer. This approach significantly improves performance for large-scale image analysis tasks.

Keywords:

Cray XMT-2 image segmentation massively parallel processing maxflow multithreading

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Area of Science:

Computer Vision
Computational Science
Graph Theory

Background:

Image segmentation is crucial for digital image analysis.
The max-flow min-cut method is effective for image segmentation.
Traditional max-flow algorithms are difficult to parallelize efficiently.

Purpose of the Study:

To implement and evaluate the Goldberg-Tarjan preflow-push algorithm on the Cray XMT-2 supercomputer.
To assess the performance of this parallel implementation for image segmentation.

Main Methods:

Implementation of the Goldberg-Tarjan preflow-push algorithm on the Cray XMT-2.
Utilizing the Cray XMT-2's massively multithreaded architecture and shared memory.
Timing experiments on synthetic and real-world images of varying sizes.

Main Results:

Demonstrated very good performance for the preflow-push algorithm on large images.
Successfully parallelized a graph-theoretic algorithm on a massively multithreaded architecture.
Processed images up to 32000^2 pixels, exceeding previous literature limits.

Conclusions:

The Cray XMT-2 is well-suited for parallelizing graph algorithms like preflow-push.
This implementation paves the way for practical, large-scale image analysis in production settings.
The optimized algorithm offers significant performance gains for complex image segmentation tasks.