Functional Approach to Acceleration of Monte Carlo Simulation for American Option Pricing

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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 an implementation optimized by NVIDIA CUDA engineers. In absolute terms, we can price a put option with 1 million simulation paths and 100 time steps in 20ms on a NVIDIA Tesla V100 GPU.

Presenter: Wojciech Michal Pawlak
Authors: Wojciech Michal Pawlak, Martin Elsman, Cosmin Oancea
  1. ACM SIGPLAN
  2. ICFP'19
  3. functional programming
  4. international conference on functional programming
  5. formal reasoning
  6. functional
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