Fast and energy-efficient derivatives risk analysis: Streaming option Greeks on Xilinx and Intel FPGAs
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| Format: | Preprint |
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2022
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| _version_ | 1866917580669714432 |
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| author | Klaisoongnoen, Mark Brown, Nick Brown, Oliver |
| author_facet | Klaisoongnoen, Mark Brown, Nick Brown, Oliver |
| contents | Whilst FPGAs have enjoyed success in accelerating high-frequency financial workloads for some time, their use for quantitative finance, which is the use of mathematical models to analyse financial markets and securities, has been far more limited to-date. Currently, CPUs are the most common architecture for such workloads, and an important question is whether FPGAs can ameliorate some of the bottlenecks encountered on those architectures. In this paper we extend our previous work accelerating the industry standard Securities Technology Analysis Center's (STAC\textregistered) derivatives risk analysis benchmark STAC-A2\texttrademark{}, by first porting this from our previous Xilinx implementation to an Intel Stratix-10 FPGA, exploring the challenges encountered when moving from one FPGA architecture to another and suitability of techniques. We then present a host-data-streaming approach that ultimately outperforms our previous version on a Xilinx Alveo U280 FPGA by up to 4.6 times and requiring 9 times less energy at the largest problem size, while outperforming the CPU and GPU versions by up to 8.2 and 5.2 times respectively. The result of this work is a significant enhancement in FPGA performance against the previous version for this industry standard benchmark running on both Xilinx and Intel FPGAs, and furthermore an exploration of optimisation and porting techniques that can be applied to other HPC workloads. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2212_13977 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Fast and energy-efficient derivatives risk analysis: Streaming option Greeks on Xilinx and Intel FPGAs Klaisoongnoen, Mark Brown, Nick Brown, Oliver Distributed, Parallel, and Cluster Computing Hardware Architecture Mathematical Software Whilst FPGAs have enjoyed success in accelerating high-frequency financial workloads for some time, their use for quantitative finance, which is the use of mathematical models to analyse financial markets and securities, has been far more limited to-date. Currently, CPUs are the most common architecture for such workloads, and an important question is whether FPGAs can ameliorate some of the bottlenecks encountered on those architectures. In this paper we extend our previous work accelerating the industry standard Securities Technology Analysis Center's (STAC\textregistered) derivatives risk analysis benchmark STAC-A2\texttrademark{}, by first porting this from our previous Xilinx implementation to an Intel Stratix-10 FPGA, exploring the challenges encountered when moving from one FPGA architecture to another and suitability of techniques. We then present a host-data-streaming approach that ultimately outperforms our previous version on a Xilinx Alveo U280 FPGA by up to 4.6 times and requiring 9 times less energy at the largest problem size, while outperforming the CPU and GPU versions by up to 8.2 and 5.2 times respectively. The result of this work is a significant enhancement in FPGA performance against the previous version for this industry standard benchmark running on both Xilinx and Intel FPGAs, and furthermore an exploration of optimisation and porting techniques that can be applied to other HPC workloads. |
| title | Fast and energy-efficient derivatives risk analysis: Streaming option Greeks on Xilinx and Intel FPGAs |
| topic | Distributed, Parallel, and Cluster Computing Hardware Architecture Mathematical Software |
| url | https://arxiv.org/abs/2212.13977 |