Towards whole program generation of quadrature-free discontinuous Galerkin methods for the shallow water equations. Faghih-Naini, S.; Kuckuk, S.; Aizinger, V.; Zint, D.; Grosso, R.; and Köstler, H. arXiv:1904.08684 [cs], April, 2019. arXiv: 1904.08684
Towards whole program generation of quadrature-free discontinuous Galerkin methods for the shallow water equations [link]Paper  abstract   bibtex   
The shallow water equations (SWE) are a commonly used model to study tsunamis, tides, and coastal ocean circulation. However, there exist various approaches to discretize and solve them efficiently. Which of them is best for a certain scenario is often not known and, in addition, depends heavily on the used HPC platform. From a simulation software perspective, this places a premium on the ability to adapt easily to different numerical methods and hardware architectures. One solution to this problem is to apply code generation techniques and to express methods and specific hardware-dependent implementations on different levels of abstraction. This allows for a separation of concerns and makes it possible, e.g., to exchange the discretization scheme without having to rewrite all low-level optimized routines manually. In this paper, we show how code for an advanced quadrature-free discontinuous Galerkin (DG) discretized shallow water equation solver can be generated. Here, we follow the multi-layered approach from the ExaStencils project that starts from the continuous problem formulation, moves to the discrete scheme, spells out the numerical algorithms, and, finally, maps to a representation that can be transformed to a distributed memory parallel implementation by our in-house Scala-based source-to-source compiler. Our contributions include: A new quadrature-free discontinuous Galerkin formulation, an extension of the class of supported computational grids, and an extension of our toolchain allowing to evaluate discrete integrals stemming from the DG discretization implemented in Python. As first results we present the whole toolchain and also demonstrate the convergence of our method for higher order DG discretizations.
@article{faghih-naini_towards_2019,
	title = {Towards whole program generation of quadrature-free discontinuous {Galerkin} methods for the shallow water equations},
	url = {http://arxiv.org/abs/1904.08684},
	abstract = {The shallow water equations (SWE) are a commonly used model to study tsunamis, tides, and coastal ocean circulation. However, there exist various approaches to discretize and solve them efficiently. Which of them is best for a certain scenario is often not known and, in addition, depends heavily on the used HPC platform. From a simulation software perspective, this places a premium on the ability to adapt easily to different numerical methods and hardware architectures. One solution to this problem is to apply code generation techniques and to express methods and specific hardware-dependent implementations on different levels of abstraction. This allows for a separation of concerns and makes it possible, e.g., to exchange the discretization scheme without having to rewrite all low-level optimized routines manually. In this paper, we show how code for an advanced quadrature-free discontinuous Galerkin (DG) discretized shallow water equation solver can be generated. Here, we follow the multi-layered approach from the ExaStencils project that starts from the continuous problem formulation, moves to the discrete scheme, spells out the numerical algorithms, and, finally, maps to a representation that can be transformed to a distributed memory parallel implementation by our in-house Scala-based source-to-source compiler. Our contributions include: A new quadrature-free discontinuous Galerkin formulation, an extension of the class of supported computational grids, and an extension of our toolchain allowing to evaluate discrete integrals stemming from the DG discretization implemented in Python. As first results we present the whole toolchain and also demonstrate the convergence of our method for higher order DG discretizations.},
	urldate = {2019-04-23},
	journal = {arXiv:1904.08684 [cs]},
	author = {Faghih-Naini, Sara and Kuckuk, Sebastian and Aizinger, Vadym and Zint, Daniel and Grosso, Roberto and Köstler, Harald},
	month = apr,
	year = {2019},
	note = {arXiv: 1904.08684},
	keywords = {Computer Science - Computational Engineering, Finance, and Science, Computer Science - Mathematical Software, mentions sympy},
}
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