A Survey of High Level Frameworks in Block-Structured Adaptive Mesh Refinement Packages

October 27, 2016 Β· The Cartographer Β· πŸ› J. Parallel Distributed Comput.

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Survey/review paper β€” maps the landscape rather than implementing a method.

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"Title-pattern auto-detect: A Survey of High Level Frameworks in Block-Structured Adaptive Mesh Refinement Packages"

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Authors Anshu Dubey, Ann Almgren, John Bell, Martin Berzins, Steve Brandt, Greg Bryan, Phillip Colella, Daniel Graves, Michael Lijewski, Frank LΓΆffler, Brian O'Shea, Erik Schnetter, Brian Van Straalen, Klaus Weide arXiv ID 1610.08833 Category cs.DC: Distributed Computing Cross-listed astro-ph.HE, gr-qc Citations 149 Venue J. Parallel Distributed Comput. Last Checked 8 days ago
Abstract
Over the last decade block-structured adaptive mesh refinement (SAMR) has found increasing use in large, publicly available codes and frameworks. SAMR frameworks have evolved along different paths. Some have stayed focused on specific domain areas, others have pursued a more general functionality, providing the building blocks for a larger variety of applications. In this survey paper we examine a representative set of SAMR packages and SAMR-based codes that have been in existence for half a decade or more, have a reasonably sized and active user base outside of their home institutions, and are publicly available. The set consists of a mix of SAMR packages and application codes that cover a broad range of scientific domains. We look at their high-level frameworks, and their approach to dealing with the advent of radical changes in hardware architecture. The codes included in this survey are BoxLib, Cactus, Chombo, Enzo, FLASH, and Uintah.
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