research-article

Acceleration of lattice models for pricing portfolios of fixed-income derivatives

Authors:

Wojciech Michal Pawlak,

Martin Metaksov,

Cosmin Eugen OanceaAuthors Info & Claims

ARRAY 2021: Proceedings of the 7th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming

Pages 27 - 38

https://doi.org/10.1145/3460944.3464309

Published: 18 June 2021 Publication History

Abstract

This paper reports on the acceleration of a standard, lattice-based numerical algorithm that is widely used in finance for pricing a class of fixed-income vanilla derivatives.

We start with a high-level algorithmic specification, exhibiting irregular nested parallelism, which is challenging to map efficiently to GPU hardware. From it we systematically derive and optimize two CUDA implementations, which utilize only the outer or all levels of parallelism, respectively. A detailed evaluation demonstrates (i) the high impact of the proposed optimizations, (ii) the complementary strength and weaknesses of the two GPU versions, and that (iii) they are on average 2.4× faster than our well-tuned CPU-parallel implementation (OpenMP+AVX2) running on 104 hardware threads, and by 3-to-4 order of magnitude faster than an OpenMP-parallel implementation using the popular QuantLib library.

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Cited By

Serykh DOehmcke SOancea CMasiliūnas DVerbesselt JCheng YHorion SGieseke FHinnerskov N(2023)Seasonal-Trend Time Series Decomposition on Graphics Processing Units2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386208(5914-5923)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386208

Index Terms

Acceleration of lattice models for pricing portfolios of fixed-income derivatives
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics
2. Computing methodologies
  1. Modeling and simulation
    1. Simulation types and techniques
      1. Massively parallel and high-performance simulations
  2. Parallel computing methodologies
    1. Parallel algorithms
      1. Massively parallel algorithms
      2. Shared memory algorithms
    2. Parallel programming languages

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cover image ACM Conferences

ARRAY 2021: Proceedings of the 7th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming

June 2021

50 pages

ISBN:9781450384667

DOI:10.1145/3460944

General Chairs:
Tze Meng Low
Carnegie Mellon University, USA
,
Jeremy Gibbons
University of Oxford, UK

Copyright © 2021 ACM.

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Published: 18 June 2021

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PLDI '21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

June 21, 2021

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Cited By

Serykh DOehmcke SOancea CMasiliūnas DVerbesselt JCheng YHorion SGieseke FHinnerskov N(2023)Seasonal-Trend Time Series Decomposition on Graphics Processing Units2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386208(5914-5923)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386208

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