Leveraging OpenSPL for Financial Risk Computation
Across all industries, there is a drive to process information in less time, with greater predictability and with higher efficiency. As the datasets and scenarios continue to grow, data doesn't only become big, it gets heavy, which increases the need to discover methods for higher degrees of parallelism without sacrificing time to market. Beyond software solutions, Finance is increasingly turning to hardware acceleration, but this poses additional challenges in terms of portability and flexibility. During this session we will explore the use of the dataflow computing language OpenSPL and its use in FPGAs for computational finance methods. Through example, this session will provide an overview of the language and expose you to the natural parallelism of dataflow computing using some well-known risk management algorithms like Black Scholes, SPAN and Value at Risk.