Computation of Voronoi diagrams using a graphics processing unit

Majdandzic, Igor; Trefftz, Christian; Wolffe, Greg

doi:10.1109/eit.2008.4554342

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…As benchmark, we developed in CUDA a Voronoi diagram generator, according to the raster-coloring massively parallel approach [23]. Informally, a Voronoi diagram for a 2D-plane and K points on it, divides the plane into tiles, each tile consisting of the points in the plane closer to one of the K points than to any other.…”

Section: Methodsmentioning

confidence: 99%

WCET Measurement-based and Extreme Value Theory Characterisation of CUDA Kernels

Berezovskyi

Santinelli

Bletsas

et al. 2014

Proceedings of the 22nd International Conference on Real-Time Networks and Systems

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Over the last few years, considerable interest has arisen in measurement-based probabilistic timing analysis. The term MBPTA has been used to indistinctly refer to a variety of different applications of Extreme Value Theory (EVT) to the timing analysis problem. The successful application of MBPTA techniques to a score of case studies has not fully dispelled the concerns that industrial stakeholders had with the quality of the computed bounds, hence ultimately with their industrial viability. Placing focus on the MBPTA methods and techniques developed in the PROARTIS and PROXIMA projects, collectively referred to as proMBPTA, we discuss the main misconceptions and pitfalls that can prevent a sound application of EVT-based WCET analysis. Using a combination of arguments and support examples, we show that proMBPTA is a rigorous process, fully amenable to sound and sustainable industrial use.

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Section: Methodsmentioning

confidence: 99%

WCET Measurement-based and Extreme Value Theory Characterisation of CUDA Kernels

Berezovskyi

Santinelli

Bletsas

et al. 2014

Proceedings of the 22nd International Conference on Real-Time Networks and Systems

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“…Majdandzic et al presented an algorithm to compute discrete Voronoi diagrams using NVIDIA's Compute Unified Device Architecture (CUDA) [Majdandzic et al 2008]. By using Voronoi diagrams, the GAMMA group computed surface distance maps for each agent, which encodes the distance to the closest neighbors [Sud et al 2007b].…”

Section: Related Workmentioning

confidence: 99%

“…Given a set of S seed points, a Voronoi diagram is a division of a space into |S| tiles, called Voronoi tiles, such that all points p inside a tile are closer to a particular seed than to any other seed [Majdandzic et al 2008…”

Section: Truncated Voronoi Diagramsmentioning

confidence: 99%

Proximity Queries for Crowd Simulation Using Truncated Voronoi Diagrams

Gyves

Toledo

Rudomín

2013

Proceedings of Motion on Games

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Crowd simulation has been used in the entertainment industry to create populated environments that seem more realistic than isolated scenes, specially in urban areas. Video games are no exception, but the simulation of large crowds is often restrained by the remaining compute time available in CPUs, which is often used for most part of the game loop. This paper presents a parallel technique, using consumer grade graphics hardware, for proximity queries that is suitable for real-time crowd simulations. We use a truncated Voronoi diagram and a sampling technique, using ray marching, to find agents' neighbors in an environment texture. The experimental results suggest that our technique has significantly better performance than similar methods and can achieve simulations with thousands of agents in interactive frame rate.

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“…The Voronoi diagram algorithm has recently been implemented using CUDA on an NVIDIA GeForce GPU and the complexity of the parallel algorithm has been shown to be linear on the number of seeds [16].…”

Section: Parallelized M-clustering Algorithmmentioning

confidence: 99%

Accelerated Discovery of Discrete M-Clusters/Outliers on the Raster Plane Using Graphical Processing Units

Trefftz

Szakas

Majdandzic

et al. 2009

Lecture Notes in Computer Science

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Abstract. This paper presents two discrete computational geometry algorithms designed for execution on Graphics Processing Units (GPUs). The algorithms are parallelized versions of sequential algorithms intended for application in geographical data mining. The first algorithm finds clusters of m points, called m-clusters, in the rasterized plane. The second algorithm complements the first by identifying outliers, those points which are not members of any m-clusters. The use of a raster representation of coordinates provides an ideal data stream environment for efficient GPU utilization. The parallel algorithms have low memory demands, and require only a limited amount of inter-process communication. Initial performance analysis indicates the algorithms are scalable, both in problem size and in the number of seeds, and significantly outperform commercial implementations.

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Computation of Voronoi diagrams using a graphics processing unit

Cited by 11 publications

References 5 publications

WCET Measurement-based and Extreme Value Theory Characterisation of CUDA Kernels

WCET Measurement-based and Extreme Value Theory Characterisation of CUDA Kernels

Proximity Queries for Crowd Simulation Using Truncated Voronoi Diagrams

Accelerated Discovery of Discrete M-Clusters/Outliers on the Raster Plane Using Graphical Processing Units

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