Acceleration of lattice models for pricing portfolios of fixed-income derivatives
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | ARRAY 2021 - Proceedings of the 7th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming, co-located with PLDI 2021 |
Editors | Tze Meng Low, Jeremy Gibbons |
Number of pages | 12 |
Publisher | Association for Computing Machinery, Inc. |
Publication date | 2021 |
Pages | 27-38 |
Article number | 3464309 |
ISBN (Electronic) | 978-1-4503-8466-7 |
DOIs | |
Publication status | Published - 2021 |
Event | 7th ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, ARRAY 2021, held in association with PLDI 2021 - Virtual, Online, Canada Duration: 21 Jun 2021 → … |
Conference
Conference | 7th ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, ARRAY 2021, held in association with PLDI 2021 |
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Land | Canada |
By | Virtual, Online |
Periode | 21/06/2021 → … |
Sponsor | ACM SIGPLAN |
Bibliographical note
Publisher Copyright:
© 2021 ACM.
- Compilers, Computational Finance, Derivative Pricing, GPGPU (Parallel) Programming
Research areas
ID: 306899886