492 research outputs found
A parallel edge orientation algorithm for quadrilateral meshes
One approach to achieving correct finite element assembly is to ensure that
the local orientation of facets relative to each cell in the mesh is consistent
with the global orientation of that facet. Rognes et al. have shown how to
achieve this for any mesh composed of simplex elements, and deal.II contains a
serial algorithm to construct a consistent orientation of any quadrilateral
mesh of an orientable manifold.
The core contribution of this paper is the extension of this algorithm for
distributed memory parallel computers, which facilitates its seamless
application as part of a parallel simulation system.
Furthermore, our analysis establishes a link between the well-known
Union-Find algorithm and the construction of a consistent orientation of a
quadrilateral mesh. As a result, existing work on the parallelisation of the
Union-Find algorithm can be easily adapted to construct further parallel
algorithms for mesh orientations.Comment: Second revision: minor change
Parallel Architectures and Parallel Algorithms for Integrated Vision Systems
Computer vision is regarded as one of the most complex and computationally intensive problems. An integrated vision system (IVS) is a system that uses vision algorithms from all levels of processing to perform for a high level application (e.g., object recognition). An IVS normally involves algorithms from low level, intermediate level, and high level vision. Designing parallel architectures for vision systems is of tremendous interest to researchers. Several issues are addressed in parallel architectures and parallel algorithms for integrated vision systems
Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable
There has been significant recent interest in parallel graph processing due
to the need to quickly analyze the large graphs available today. Many graph
codes have been designed for distributed memory or external memory. However,
today even the largest publicly-available real-world graph (the Hyperlink Web
graph with over 3.5 billion vertices and 128 billion edges) can fit in the
memory of a single commodity multicore server. Nevertheless, most experimental
work in the literature report results on much smaller graphs, and the ones for
the Hyperlink graph use distributed or external memory. Therefore, it is
natural to ask whether we can efficiently solve a broad class of graph problems
on this graph in memory.
This paper shows that theoretically-efficient parallel graph algorithms can
scale to the largest publicly-available graphs using a single machine with a
terabyte of RAM, processing them in minutes. We give implementations of
theoretically-efficient parallel algorithms for 20 important graph problems. We
also present the optimizations and techniques that we used in our
implementations, which were crucial in enabling us to process these large
graphs quickly. We show that the running times of our implementations
outperform existing state-of-the-art implementations on the largest real-world
graphs. For many of the problems that we consider, this is the first time they
have been solved on graphs at this scale. We have made the implementations
developed in this work publicly-available as the Graph-Based Benchmark Suite
(GBBS).Comment: This is the full version of the paper appearing in the ACM Symposium
on Parallelism in Algorithms and Architectures (SPAA), 201
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