Internal Design of Closed Form Heston¶
Heston models the dynamics of a financial market; it adds a stochastic volatility aspect to the Black-Scholes model.
The Heston Partial Differential Equation (PDE) for the price, and the closed-form equations for its solution, are given in the “Models” and “Methods” sections here for the Heston Model.
There are two layers to the kernel; the engine itself and the IO wrapper.
The Engine (hcf_engine.hpp)¶
The engine performs a single Heston Closed Form solution for a European Call Option.
It is template to generate either a floating point (Float-32) kernel or a double (Float-64) kernel. The double kernel will be more accurate but takes up more FPGA resource. The code contains some HLS directives which ensure the design is pipeline and parallel in order to improve its performance. It uses complex number arithmetic which is implemented in wrapper functions by using the HLS math library.
The integration is performed using the trapezoidal rule with a configurable dw (the integration time step) and Wmax (the upper limit of the integral). These parameters are configurable at run time and passed to the kernel along with the Heston parameters.
IO Wrapper (hcf_kernel.cpp)¶
The wrapper takes the input of a parameter array, and it iterates through the array calling the Engine for each entry. The results are returned also as an array in order to make full use of DMA in the FPGA. Because a batch data transaction is much faster than multiple single transactions. The data is firstly read from global memory into local memory, then processed in kernel and finally returned from local memory back to global memory. This is done because the extra time required by the data copies is more than compensation by speedup the Engine in accessing local memory.
The floating point kernel Area Information:
Data Type FF LUT DSP BRAM URAM Float 195663 211957 1149 28 0
The double kernel Area Information:
Data Type FF LUT DSP BRAM URAM Double 644664 654672 3849 520 0
The theoretical throughput depends on different factors. A floating point kernel will be faster than a double kernel. A smaller dw and larger Wmax will provide more accurate results but will decrease throughput. The kernel has been pipelined in order to increase the throughput when a large number of input needs to be processed.
Throughput is composed of three processes; transferring data to the FPGA, running the computations and transferring the results back from the FPGA. The demo contains options to measure timings as described in the README.md file.
As an example, processing a batch of 1000 call calculations with a floating point kernel with dw = 0.5 and Wmax = 200 breaks down as follows:
Time to transfer data = 0.26ms
Time for 1000 calculations = 14.4ms (equates to 14.4us per calculation)
Time to transfer results = 0.18ms