ICFP 2019 (series) / FHPNC 2019 (series) / Functional High-Performance and Numerical Computing /
Functional Approach to Acceleration of Monte Carlo Simulation for American Option Pricing (extended 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 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.
Sun 18 AugDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
Sun 18 Aug
Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:50 - 12:10 | |||
10:50 26mTalk | Generating Efficient FFT GPU Code with Lift FHPNC Link to publication DOI Pre-print File Attached | ||
11:16 26mTalk | Lazy Evaluation in Infinite-Dimensional Function Spaces with Wavelet Basis FHPNC Link to publication Pre-print | ||
11:43 26mTalk | Functional Approach to Acceleration of Monte Carlo Simulation for American Option Pricing (extended abstract) FHPNC Wojciech Michal Pawlak University of Copenhagen, Denmark, Martin Elsman University of Copenhagen, Denmark, Cosmin Oancea University of Copenhagen, Denmark Link to publication | ||