Improving data locality in parallel fast Fourier transform algorithm for pricing financial derivatives

Summary form only given. Pricing of derivatives is one of the central problems in computational finance. Since the theory of derivative pricing is highly mathematical, numerical techniques such as binomial lattice, finite-differencing and fast Fourier transform (FFT) among others have been used for derivative or option pricing. Based on a recent work on FFT for VLSI circuits, we develop a parallel algorithm in the current work, which improves data locality and hence reduce communication overheads. Our main aim is to study the performance of this algorithm. Compared to the traditional butterfly network, the current algorithm with data swap network performs better by more than 15% for large data sizes.