### Abstract

MuPAT, an interactive multiple precision arithmetic toolbox for use on MATLAB and Scilab, enables users to handle quadruple- and octuple-precision arithmetic operations. MuPAT uses the DD and QD algorithms, which require from 10 to 600 double-precision floating-point operations for each DD or QD operation, which entails corresponding execution time costs. In order to reduce the execution time of vector and matrix operations, we apply FMA, AVX2, and OpenMP to MuPAT by using the MATLAB executable file. Unit stride access is required for high performance and it makes vectorization with AVX2 easier. Larger blocks are suitable for parallelization with OpenMP. That is, AVX2 is suitable for the innermost loop and OpenMP is suitable for the outer loop. One result of adopting the described configuration is that matrix multiplication is nearly 13 times faster in a four-core environment. By using parallel processing in this way, the execution time of some DD vector operations is almost twice that of the original double-precision floating-point operations without parallel processing.

Original language | English |
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Title of host publication | Parallel Computing |

Subtitle of host publication | Technology Trends |

Editors | Ian Foster, Gerhard R. Joubert, Ludek Kucera, Wolfgang E. Nagel, Frans Peters |

Publisher | IOS Press BV |

Pages | 431-440 |

Number of pages | 10 |

ISBN (Electronic) | 9781643680705 |

DOIs | |

Publication status | Published - 1 Jan 2020 |

### Publication series

Name | Advances in Parallel Computing |
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Volume | 36 |

ISSN (Print) | 0927-5452 |

ISSN (Electronic) | 1879-808X |

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### Keywords

- AVX2
- DD
- Double-Double
- MATLAB
- Multicore

### Cite this

*Parallel Computing: Technology Trends*(pp. 431-440). (Advances in Parallel Computing; Vol. 36). IOS Press BV. https://doi.org/10.3233/APC200069