# Difference between revisions of "SC²S Colloquium - October 21, 2016"

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TBA | TBA | ||

− | == Benedikt Kucis: A GPU-accelerated Chebyshev | + | == Benedikt Kucis: A GPU-accelerated Chebyshev Function Approximation on an Adaptive Tree Structure == |

This bachelor’s thesis describes the process of designing and implementing a GPU-accelerated | This bachelor’s thesis describes the process of designing and implementing a GPU-accelerated | ||

Chebyshev function approximation on an Adaptive Tree Structure. The adaptive trees are used for storing Chebyshev coefficients computed using an input function for e.g. vorticity fields or Gaussian functions. Chebyshev points are optimal interpolation points to approximate smooth functions over a specified domain. The evaluation of the Chebyshev interpolant for an arbitrary point is computationally expensive. Because the Chebyshev approximation can be heavily parallelized, the goal of this work was to speed up the evaluation by utilizing the massively parallel architecture of GPUs. Existing CPU implementations in the PVFMM and TbSLAS libraries are explained and the process of implementing and optimizing the GPU version of the evaluation is described. The largest achieved speedup was about 2X the CPU performance. | Chebyshev function approximation on an Adaptive Tree Structure. The adaptive trees are used for storing Chebyshev coefficients computed using an input function for e.g. vorticity fields or Gaussian functions. Chebyshev points are optimal interpolation points to approximate smooth functions over a specified domain. The evaluation of the Chebyshev interpolant for an arbitrary point is computationally expensive. Because the Chebyshev approximation can be heavily parallelized, the goal of this work was to speed up the evaluation by utilizing the massively parallel architecture of GPUs. Existing CPU implementations in the PVFMM and TbSLAS libraries are explained and the process of implementing and optimizing the GPU version of the evaluation is described. The largest achieved speedup was about 2X the CPU performance. |

## Revision as of 16:09, 5 October 2016

Date: |
October 21, 2016 |

Room: |
02.07.023 |

Time: |
3:00 pm, s.t. |

## Tobias Neuhauser: A Method for Simulation-based Parameter Optimization of Autonomous Emergency Braking

TBA

## Benedikt Kucis: A GPU-accelerated Chebyshev Function Approximation on an Adaptive Tree Structure

This bachelor’s thesis describes the process of designing and implementing a GPU-accelerated Chebyshev function approximation on an Adaptive Tree Structure. The adaptive trees are used for storing Chebyshev coefficients computed using an input function for e.g. vorticity fields or Gaussian functions. Chebyshev points are optimal interpolation points to approximate smooth functions over a specified domain. The evaluation of the Chebyshev interpolant for an arbitrary point is computationally expensive. Because the Chebyshev approximation can be heavily parallelized, the goal of this work was to speed up the evaluation by utilizing the massively parallel architecture of GPUs. Existing CPU implementations in the PVFMM and TbSLAS libraries are explained and the process of implementing and optimizing the GPU version of the evaluation is described. The largest achieved speedup was about 2X the CPU performance.