金融交易：数据中心FPGA的简短计算金融案例研究
Algorithmic Trading: A brief, computational finance case study on data centre FPGAs
摘要：
由于对高能效计算和改进的高级综合工具流程的需求增加，FPGA将越来越多地大规模部署，从而创建了一种新的设备类别：数据中心FPGA。使用这些FPGA的方法是确定给定工作负载的多大比例将受益于在可用FPGA上实现，同时最小化片外通信。作为这些任务实现的一部分，应注意识别应使用的并行执行模式、任务或流水线并行。当考虑计算金融任务的案例研究，赫斯顿和Black-Scholes的基准工作量的选项使用OpenCL和OpenSPL实现时，这种方法使用数据中心FPGA的好处说明。这些器件提供的延迟性能接近工作站级GPU，同时所需的能量大大减少，从而使每焦耳能耗的浮点运算增加30%。
英文摘要：
Increasingly FPGAs will be deployed at scale due to the need for increased need for power efficient computation and improved high level synthesis tool flows, creating a new category of device: data centre FPGAs. A method for using these FPGAs is to identify what proportion of a given workload would benefit from being implemented upon the available FPGAs while minimising communication off-chip. As part of the implementation of these tasks, care should be taken in identifying the parallel execution mode, task or pipeline parallelism that should be used. When considering a case study of computational finance tasks, a benchmark workload of Heston and Black-Scholes-based options implemented using OpenCL and OpenSPL, the benefit of this method of using data centre FPGAs is illustrated. These devices deliver latency performance close to that of workstation grade GPUs, while requiring considerably less energy, resulting in 30% more floating point operations per Joule of energy consumed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
