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Distributed Monte Carlo Simulation for Option Pricing - The First Completed Benchmark and Applications of Distributed Monte Carlo Simulation Model on High-performance Computing Architecture

机译:用于期权定价的分布式蒙特卡罗模拟 - 高性能计算架构分布式蒙特卡罗仿真模型的第一个完成基准和应用

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As financial institution computing requirements grow exponentially, we have explored the potential for the ClearSpeed Accelerator, the Cell processor and the FPGA (A field-programmable gate array) to run risk analytics applications. We also invented the Smoothed Alias Method based generator for FPGA in order to achieve the fast result. We have taken Monte Carlo algorithm from my C++ Quantitative Library and rewrite it for this benchmark purpose and test the algorithm with some clever numerical adaptation with the Bulk Synchronous Parallel (BSP) computing model in order to leverage the distributed computing architecture. Following the initial benchmark, we have chosen to use the ClearSpeed Accelerator. With some smart quant re-engineering, we have further optimized the Distributed MC algorithm for pricing Bermudan Swaption to exploit the potential of distributed-based architecture. We will show the comparative benchmark results of the MC algorithm on ClearSpeed Accelerator, Cell and FPGA platform for the first time within our industry based on my working notes from my time in Barclays Capital London.
机译:由于金融机构计算要求呈指数级增长,我们探索了ClearSpeed加速器,单元处理器和FPGA(现场可编程门阵列)的潜力来运行风险分析应用。我们还发明了用于FPGA的基于平滑的别名方法,以实现快速结果。我们已经从我的C ++定量库中拍摄了Monte Carlo算法,并将其重写为此基准目的,并通过批量同步并行(BSP)计算模型的一些巧妙的数值适应来测试算法,以利用分布式计算架构。遵循初始基准,我们已选择使用ClearSpeed加速器。通过一些智能量子重新设计,我们进一步优化了分布式MC算法,用于定价百慕大仪器,以利用分布式架构的潜力。我们将在我们的行业内第一次基于我的工作票据,在Marclays Capital London的时间内首次展示Clearspeed加速器,Cell和FPGA平台上MC算法的比较基准结果。

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