CUDA cuts: Fast graph cuts on the GPU

Vineet, Vibhav; Narayanan, P. J.

doi:10.1109/cvprw.2008.4563095

Cited by 183 publications

(128 citation statements)

References 28 publications

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“…Indeed, past work [5,8,10,12,16] demonstrates that GPU offloading offers tangible benefits compared to traditional multiprocessors for graph processing. However, previous work assumes the entire graph fits in the GPU memory.…”

Section: Graph Processing On Heterogeneous Architectures: Opportunitymentioning

confidence: 99%

A yoke of oxen and a thousand chickens for heavy lifting graph processing

Gharaibeh

Costa

Santos‐Neto

et al. 2012

Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques

124

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Large, real-world graphs are famously difficult to process efficiently. Not only they have a large memory footprint but most graph processing algorithms entail memory access patterns with poor locality, data-dependent parallelism, and a low compute-tomemory access ratio. Additionally, most real-world graphs have a low diameter and a highly heterogeneous node degree distribution. Partitioning these graphs and simultaneously achieve access locality and load-balancing is difficult if not impossible. This paper demonstrates the feasibility of graph processing on heterogeneous (i.e., including both CPUs and GPUs) platforms as a cost-effective approach towards addressing the graph processing challenges above. To this end, this work (i) presents and evaluates a performance model that estimates the achievable performance on heterogeneous platforms; (ii) introduces TOTEM -a processing engine based on the Bulk Synchronous Parallel (BSP) model that offers a convenient environment to simplify the implementation of graph algorithms on heterogeneous platforms; and, (iii) demonstrates TOTEM'S efficiency by implementing and evaluating two graph algorithms (PageRank and breadth-first search). TOTEM achieves speedups close to the model's prediction, and applies a number of optimizations that enable linear speedups with respect to the share of the graph offloaded for processing to accelerators.

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Section: Graph Processing On Heterogeneous Architectures: Opportunitymentioning

confidence: 99%

A yoke of oxen and a thousand chickens for heavy lifting graph processing

Gharaibeh

Costa

Santos‐Neto

et al. 2012

Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques

124

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“…Parallel versions of some maximum flow algorithms have been devised for multiprocessor architectures [12] and graphics processing units [13]. While these methods attain good speedups, they do not reduce memory footprint since they operate on the entire image.…”

Section: A Graph Cut In Segmentation and Its Complexitymentioning

confidence: 99%

Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut

Kéchichian

Valette

Desvignes

et al. 2013

IEEE Trans. on Image Process.

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To cite this version:Razmig Kéchichian, Sébastien Valette, Michel Desvignes, Rémy Prost. Shortest-path constraints for 3D multiobject semiautomatic segmentation via clustering and Graph Cut. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2013, 22 (11) Abstract-We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analysis and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared to a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.

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“…During recent years, many reports on CUDA implementations of a large variety of segmentation algorithms have been published. Some examples are GPU acceleration of graph cuts (Vineet and Narayanan, 2008), expectation maximization and k-means clustering for analysis of histopathological images of neuroblastoma (Ruiz et al, 2008), registration-based segmentation of MRI volumes (Han et al, 2009), liver segmentation based on Markov random fields (Walters et al, 2009), shape models for segmentation of vertebra in X-ray images (Mahmoudi et al, 2010), random walks (Collins et al, 2012), fuzzy connected image segmentation of CT and MRI volumes (Zhuge et al, 2011) and a hybrid approach to segmentation of vessel laminae from confocal microscope images (Narayanaswamy et al, 2010).…”

Section: Image Segmentationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

348

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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CUDA cuts: Fast graph cuts on the GPU

Abstract: Abstract

Cited by 183 publications

References 28 publications

A yoke of oxen and a thousand chickens for heavy lifting graph processing

A yoke of oxen and a thousand chickens for heavy lifting graph processing

Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut

Medical image processing on the GPU – Past, present and future

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