Quantitative finance is the use of mathematical models to analyse financial markets and securities. Typically requiring significant amounts of computation, an important question is the role that novel architectures can play in accelerating these models. In this paper we explore the acceleration of the industry standard Securities Technology Analysis Center's (STAC) derivatives risk analysis benchmark STAC-A2\texttrademark{} by porting the Heston stochastic volatility model and Longstaff and Schwartz path reduction onto a Xilinx Alveo U280 FPGA with a focus on efficiency-driven computing. Describing in detail the steps undertaken to optimise the algorithm for the FPGA, we then leverage the flexibility provided by the reconfigurable architecture to explore choices around numerical precision and representation. Insights gained are then exploited in our final performance and energy measurements, where for the efficiency improvement metric we achieve between an 8 times and 185 times improvement on the FPGA compared to two 24-core Intel Xeon Platinum CPUs. The result of this work is not only a show-case for the market risk analysis workload on FPGAs, but furthermore a set of efficiency driven techniques and lessons learnt that can be applied to quantitative finance and computational workloads on reconfigurable architectures more generally.

翻译：量化融资是使用数学模型来分析金融市场和证券。 通常需要大量计算,一个重要问题是新架构在加速这些模型方面可以发挥的作用。 在本文中,我们探讨了加快行业标准证券技术分析中心的衍生物风险分析基准STAC-A2\texttrademark STAC-A2\ Texttrades 基准 STAC-A2\ treatmark 标准 STAC-A2\ texttraphic TRA 标准 标准 STAC-A2\ Textmark 标准 标准 标准 STAC- A2\ texttradeffiltyl 模型 和 Longstaff 和 Schwartz 路径降低到 Xilinx Alveo U280 PFPGA 模式, 重点是效率驱动的计算。 这项工作的结果不仅是详细说明了优化FPGA的算法的算法, 然后我们利用可重新配置的架构所提供的灵活性来探索关于数字精确性和代表性的选择。 我们最后的绩效和能源测量方法得到了利用,为了提高效率,我们在两个24核心的InXeon Platinum CPum CPU CP 的计算方法上, 。