Fast and energy-efficient derivatives risk analysis: Streaming option Greeks on Xilinx and Intel FPGAs

TL;DR

This paper demonstrates that FPGA-based acceleration of derivatives risk analysis significantly outperforms CPU and GPU implementations, achieving up to 8.2 times faster performance and 9 times lower energy consumption by porting and optimizing across different FPGA architectures.

Contribution

The paper introduces a host-data-streaming approach for FPGA acceleration of derivatives risk analysis, outperforming previous implementations and exploring cross-architecture porting techniques.

Findings

Up to 4.6x performance improvement on Xilinx FPGA

Up to 8.2x speedup over CPU and GPU

9x reduction in energy consumption

Abstract

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.