Parallel Compact Hash Algorithms for Computational Meshes
Abstract
We employ compact hashing and the discrete properties of computational meshes to optimize spatial operations in scientific computing applications. Our target is to develop highly parallel compact hashing methods suitable for the fine-grained parallelism of GPU and MIC architectures that will scale to the next generation of computing systems. As a model, we apply spatial hashing methods to the problem of determining neighbor elements in adaptive mesh refinement (AMR) schemes. By applying memory savings techniques, we extend the perfect spatial hash algorithm to a compact hash by compressing the resulting sparse data structures. Using compact hashing and specific memory optimizations, we increase the range of problems that can benefit from our ideal $O(n)$ algorithms. The spatial hash methods are tested and compared across a variety of architectures on both a randomly generated sample mesh and an existing cell-based AMR shallow-water hydrodynamics scheme. We demonstrate consistent speed-up and increased performance across every device tested and explore the ubiquitous application of spatial hashing in scientific computing.
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Title of paper: Parallel Compact Hash Algorithms for Computational Meshes
Authors: Tumblin, R., Ahrens, W., Hartse, S., and Robey, R.W.
File: compacthash-v1.1.zip
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Contents: Source code used for article
Justification: The source code has been released under the Apache open-source license so SIAM readers can reproduce the work from the article and extend it in the future. This snapshot was used to obtain the published results.
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© 2015, Society for Industrial and Applied Mathematics.
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Submitted: 20 August 2013
Accepted: 20 October 2014
Published online: 20 January 2015
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