A Functional Approach to Accelerating Monte Carlo based American Option Pricing
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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A Functional Approach to Accelerating Monte Carlo based American Option Pricing. / Pawlak, Wojciech Michal; Elsman, Martin; Oancea, Cosmin Eugen.
IFL 2019: Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages. Association for Computing Machinery, 2021. s. 1-12 5.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - A Functional Approach to Accelerating Monte Carlo based American Option Pricing
AU - Pawlak, Wojciech Michal
AU - Elsman, Martin
AU - Oancea, Cosmin Eugen
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
U2 - 10.1145/3412932.3412937
DO - 10.1145/3412932.3412937
M3 - Article in proceedings
SP - 1
EP - 12
BT - IFL 2019: Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages
PB - Association for Computing Machinery
Y2 - 19 September 2019
ER -
ID: 258660593