Parallel geometric algorithms for multi-core computers

Batista, Vicente H. F.; Millman, David L.; Pion, Sylvain; Singler, Johannes

doi:10.1016/j.comgeo.2010.04.008

Cited by 39 publications

(1 citation statement)

References 26 publications

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“…If some dependency cannot be satisfied, the operation releases any captured dependencies, aborts its execution (rollback), and reenters the scheduler's pool. The speculative approach has already been utilized in Delaunay triangulation methods [4,5,25], Delaunay Refinement [27] and Advancing-Front [20] methods. However, in each case, the approach was application-and method specific.…”

Section: Related Workmentioning

confidence: 99%

Tasking framework for adaptive speculative parallel mesh generation

2021

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Handling the ever-increasing complexity of mesh generation codes along with the intricacies of newer hardware often results in codes that are both difficult to comprehend and maintain. Different facets of codes such as thread management and load balancing are often intertwined, resulting in efficient but highly complex software. In this work, we present a framework which aids in establishing a core principle, deemed separation of concerns, where functionality is separated from performance aspects of various mesh operations. In particular, thread management and scheduling decisions are elevated into a generic and reusable tasking framework. The results indicate that our approach can successfully abstract the load balancing aspects of two case studies, while providing access to a plethora of different execution backends. One would expect, this new flexibility to lead to some additional cost. However, for the configurations studied in this work, we observed up to 13% speedup for some meshing operations and up to 5.8% speedup over the entire application runtime compared to hand-optimized code. Moreover, we show that by using different task creation strategies, the overhead compared to straight-forward task execution models can be improved dramatically by as much as 1200% without compromises in portability and functionality.

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Section: Related Workmentioning

confidence: 99%

Tasking framework for adaptive speculative parallel mesh generation

2021

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One machine, one minute, three billion tetrahedra

Marot

Pellerin

Remacle

2018

Numerical Meth Engineering

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This paper presents a new scalable parallelization scheme to generate the 3D Delaunay triangulation of a given set of points. Our first contribution is an efficient serial implementation of the incremental Delaunay insertion algorithm. KEYWORDS3D Delaunay triangulation, parallel delaunay, radix sort, SFC partitioning, tetrahedral mesh generation Int J Numer Methods Eng. 2019;117:967-990. wileyonlinelibrary.com/journal/nme

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Load-Balancing for Parallel Delaunay Triangulations

Funke

Sanders

Winkler

2019

Lecture Notes in Computer Science

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Computing the Delaunay triangulation (DT) of a given point set in R D is one of the fundamental operations in computational geometry. Recently, Funke and Sanders [18] presented a divide-and-conquer DT algorithm that merges two partial triangulations by re-triangulating a small subset of their vertices -the border vertices -and combining the three triangulations efficiently via parallel hash table lookups. The input point division should therefore yield roughly equal-sized partitions for good load-balancing and also result in a small number of border vertices for fast merging. In this paper, we present a novel divide-step based on partitioning the triangulation of a small sample of the input points. In experiments on synthetic and real-world data sets, we achieve nearly perfectly balanced partitions and small border triangulations. This almost cuts running time in half compared to non-data-sensitive division schemes on inputs exhibiting an exploitable underlying structure.Recently, we presented a novel divide-and-conquer (D&C) DT algorithm for arbitrary dimension [18] that lends itself equally well to shared and distributed memory parallelism and thus hybrid parallelization. While previous D&C DT algorithms suffer from a complex -often sequential -divide or merge step [12,24], our algorithm reduces the merging of two partial triangulations to re-triangulating a small subset of their vertices -the border vertices -using the same parallel algorithm and combining the three triangulations efficiently via hash table lookups. All steps required for the merging -identification of relevant vertices, triangulation and combining the partial DTs -are performed in parallel.The division of the input points in the divide-step needs to address a twofold sensitivity to the point distribution: the partitions need to be approximately equal-sized for good load-balancing, arXiv:1902.07554v1 [cs.DS]

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Parallel geometric algorithms for multi-core computers

Cited by 39 publications

References 26 publications

Tasking framework for adaptive speculative parallel mesh generation

Tasking framework for adaptive speculative parallel mesh generation

One machine, one minute, three billion tetrahedra

Load-Balancing for Parallel Delaunay Triangulations

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