Engineering a compact parallel delaunay algorithm in 3D

Blandford, Daniel K.; Blelloch, Guy E.; Kadow, Clemens

doi:10.1145/1137856.1137900

Cited by 34 publications

(29 citation statements)

References 46 publications

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“…Domain specialists have crafted handcoded parallel programs for many irregular algorithms; for example, Blandford et al describe a carefully hand-tuned implementation of Delaunay mesh generation and refinement [1]. However, most of these efforts are very problem-specific, and it is difficult to extract broadly applicable abstractions, principles, and mechanisms from such implementations.…”

Section: -Donald Knuthmentioning

confidence: 99%

“…As mentioned in the introduction, Blandford et al's implementation of Delaunay mesh refinement [1] uses an optimistic parallel execution strategy similar to the one used in the Galois system, but it rolls back conflicting computations without using logs or backup copies of modified data. However, logs or backup copies of modified data are needed for other algorithms such as event-driven simulation.…”

Section: Cautious Implementations Of Operatorsmentioning

confidence: 99%

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Structure-driven optimizations for amorphous data-parallel programs

et al. 2010

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Irregular algorithms are organized around pointer-based data structures such as graphs and trees, and they are ubiquitous in applications. Recent work by the Galois project has provided a systematic approach for parallelizing irregular applications based on the idea of optimistic or speculative execution of programs. However, the overhead of optimistic parallel execution can be substantial. In this paper, we show that many irregular algorithms have structure that can be exploited and present three key optimizations that take advantage of algorithmic structure to reduce speculative overheads. We describe the implementation of these optimizations in the Galois system and present experimental results to demonstrate their benefits. To the best of our knowledge, this is the first system to exploit algorithmic structure to optimize the execution of irregular programs.

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Section: -Donald Knuthmentioning

confidence: 99%

Section: Cautious Implementations Of Operatorsmentioning

confidence: 99%

Structure-driven optimizations for amorphous data-parallel programs

et al. 2010

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“…Another algorithm based on randomized incremental construction was devised by Blandford et al [28]. It employs a compact data structure and follows a Bowyer-Watson approach, maintaining an association between uninserted points and their containing tetrahedra [29].…”

Section: Related Workmentioning

confidence: 99%

“…Our scheme is similar to those in [27,28], but with different point location, load management mechanisms and locking strategies.…”

Section: Parallel Algorithmmentioning

confidence: 99%

Parallel geometric algorithms for multi-core computers

Batista

Millman

Pion

et al. 2009

Proceedings of the Twenty-Fifth Annual Symposium on Computational Geometry

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Computers with multiple processor cores using shared memory are now ubiquitous. In this paper, we present several parallel geometric algorithms that specifically target this environment, with the goal of exploiting the additional computing power. The algorithms we describe are (a) 2-/3-dimensional spatial sorting of points, as is typically used for preprocessing before using incremental algorithms, (b) d-dimensional axis-aligned box intersection computation, and finally (c) 3D bulk insertion of points into Delaunay triangulations, which can be used for mesh generation algorithms, or simply for constructing 3D Delaunay triangulations. For the latter, we introduce as a foundational element the design of a container data structure that both provides concurrent addition and removal operations and is compact in memory. This makes it especially well-suited for storing large dynamic graphs such as Delaunay triangulations.We show experimental results for these algorithms, using our implementations based on the Computational Geometry Algorithms Library (CGAL). This work is a step towards what we hope will become a parallel mode for CGAL, where algorithms automatically use the available parallel resources without requiring significant user intervention.

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“…A variety of parallel algorithms have been engineeered to handle input feature constraints, but with few or no theoretical guarantees on output size [9,11,28,27,5,18]. The current best theoretical results for parallel meshing with features in 3D is the algorithm of Spielman et al [31].…”

Section: Related Workmentioning

confidence: 99%

Sparse parallel Delaunay mesh refinement

Hudson

Phillips

2007

Proceedings of the Nineteenth Annual ACM Symposium on Parallel Algorithms and Architectures

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The authors recently introduced the technique of sparse mesh refinement to produce the first near-optimal sequential time bounds of O(n lg L/s+m) for inputs in any fixed dimension with piecewiselinear constraining (PLC) features. This paper extends that work to the parallel case, refining the same inputs in time O(lg(L/s) lg m) on an EREW PRAM while maintaining the work bound; in practice, this means we expect linear speedup for any practical number of processors. This is faster than the best previously known parallel Delaunay mesh refinement algorithms in two dimensions. It is the first technique with work bounds equal to the sequential case. In higher dimension, it is the first provably fast parallel technique for any kind of quality mesh refinement with PLC inputs. Furthermore, the algorithm's implementation is straightforward enough that it is likely to be extremely fast in practice.

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Engineering a compact parallel delaunay algorithm in 3D

Cited by 34 publications

References 46 publications

Structure-driven optimizations for amorphous data-parallel programs

Structure-driven optimizations for amorphous data-parallel programs

Parallel geometric algorithms for multi-core computers

Sparse parallel Delaunay mesh refinement

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