Abstract
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.
Originalsprog | Engelsk |
---|---|
Titel | IFL 2019: Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages |
Forlag | Association for Computing Machinery |
Publikationsdato | 2021 |
Sider | 1-12 |
Artikelnummer | 5 |
ISBN (Elektronisk) | 978-1-4503-7562-7/19/09… |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 28th Symposium on the Implementation and Application of Functional Programming Languages . IFL 2019 - Singapore, Singapore Varighed: 19 sep. 2019 → … |
Konference
Konference | 28th Symposium on the Implementation and Application of Functional Programming Languages . IFL 2019 |
---|---|
Land/Område | Singapore |
By | Singapore |
Periode | 19/09/2019 → … |