### Abstract

In modern physics it has become common to find the solution of a problem by solving numerically a set of PDEs. Whether solving them on a finite difference grid or by a finite element approach, the main calculations are often applied to a stencil structure. In the last decade it has become usual to work with so called big data problems where calculations are very heavy and accelerators and modern architectures are widely used. Although CPU and GPU clusters are often used to solve such problems, parallelisation of any calculation ideally starts from a single processor optimisation. Unfortunately, it is impossible to vectorise a stencil structured loop with high level instructions. In this paper we suggest a new approach to rearranging the data structure which makes it possible to apply high level vectorisation instructions to a stencil loop and which results in significant acceleration. The suggested method allows further acceleration if shared memory APIs are used. We show the effectiveness of the method by applying it to an elastic wave propagation problem on a finite difference grid. We have chosen Intel architecture for the test problem and OpenMP (Open Multi-Processing) since they are extensively used in many applications.

Original language | English |
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Pages (from-to) | 53-62 |

Number of pages | 10 |

Journal | Computer Physics Communications |

Volume | 216 |

Early online date | 1 Mar 2017 |

DOIs | |

Publication status | Published - Jul 2017 |

Externally published | Yes |

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## Profiles

## Sofya Titarenko

- Department of Computer Science - Senior Lecturer in Mathematics
- School of Computing and Engineering
- Centre for Mathematics and Data Science - Member

Person: Academic