A Functional Approach to Accelerating Monte Carlo based American Option Pricing

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Dokumenter

We study the feasibility and performance efficiency of expressing a complex financial numerical algorithm with high-level functional parallel constructs. The algorithm we investigate is a least-square regression-based Monte-Carlo simulation for pricing American options. We propose an accelerated parallel implementation in Futhark, a high-level functional data-parallel language. The Futhark language targets GPUs as the compute platform and we achieve a performance comparable to, and in particular cases up to 2.5X better than, an implementation optimised by NVIDIA CUDA engineers. In absolute terms, we can price a put option with 1 million simulation paths and 100 time steps in 17 ms on a NVIDIA Tesla V100 GPU. Furthermore, the high-level functional specification is much more accessible to the financial-domain experts than the low-level CUDA code, thus promoting code maintainability and facilitating algorithmic changes.
OriginalsprogEngelsk
TitelIFL 2019: Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages
ForlagAssociation for Computing Machinery
Publikationsdato2021
Sider1-12
Artikelnummer5
ISBN (Elektronisk)978-1-4503-7562-7/19/09…
DOI
StatusUdgivet - 2021
Begivenhed28th Symposium on the Implementation and Application of Functional Programming Languages . IFL 2019 - Singapore, Singapore
Varighed: 19 sep. 2019 → …

Konference

Konference28th Symposium on the Implementation and Application of Functional Programming Languages . IFL 2019
LandSingapore
BySingapore
Periode19/09/2019 → …

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