Computing persistent homology in Futhark
Paper i proceeding, 2021

We present a massively parallel algorithm for computing persistent homology, a concept within the field of topological data analysis, and we implement it in the purely functional array-based language Futhark, which has an efficient compiler targeting GPUs. Computing persistent homology consists of bringing a certain sparse matrix to a reduced form. We compare our implementation with OpenPH, an existing library for computing persistent homology on GPUs, and on large matrices we achieve speedups of 2.3 to 5. Our work shows both that persistent homology can be computed efficiently entirely on GPU hardware, and that Futhark can be used for this kind of sparse matrix manipulation.

functional programming

Futhark

persistent homology

GPGPU

data-parallel

sparse matrix

Författare

Erik Von Brömssen

Student vid Chalmers

FHPNC 2021 - Proceedings of the 9th ACM SIGPLAN International Workshop on Functional High-Performance and Numerical Computing, co-located with ICFP 2021

24-36

9th ACM SIGPLAN International Workshop on Functional High-Performance and Numerical Computing, FHPNC 2021
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Ämneskategorier

Datorteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1145/3471873.3472976

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Senast uppdaterat

2021-09-16