article · 2016

Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods

Qi Hua, Nail A. Gumerovb, Ramani Duraiswamia

Cited by 0Abstract and details only

Abstract

We present efficient algorithms to build data structures and the lists needed for fast multipole methods. The algorithms are capable of being efficiently implemented on both serial, data parallel GPU and on distributed architectures. With these algorithms it is possible to map the FMM efficiently on to the GPU or distributed heterogeneous CPU-GPU systems. Further, in dynamic problems, as the distribution of the particles change, the reduced cost of building the data structures improves performance. Using these algorithms, we demonstrate example high fidelity simulations with large problem sizes by using FMM on both single and multiple heterogeneous computing facilities equipped with multi-core CPU and many-core GPUs.

Cite this paper

APA 7

Hua, Q., Gumerovb, N. A., & Duraiswamia, R. (2016). Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods.

MLA 9

Hua, Qi, et al. “Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods.” 2016.

Chicago

Hua, Qi, Nail A. Gumerovb, and Ramani Duraiswamia. 2016. “Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods.”

Harvard

Hua, Q., Gumerovb, N.A. and Duraiswamia, R. (2016) ‘Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods’.

BibTeX

@article{hua2016parallel,
  title = {{Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods}},
  author = {Hua, Qi and Gumerovb, Nail A. and Duraiswamia, Ramani},
  year = {2016}
}

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