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Monte-Carlo simulation is a very widely used technique in scientific computations in general with huge computation benefits in solving problems where closed form solutions are impossible to derive. This technique is also characterized by a high degree of parallelism as a large number of different simulation paths need to be calculated, which makes it ideal for a parallel hardware implementation. This paper illustrates the benefits of such implementation in the context of financial computing as it implements a financial Monte-Carlo simulation engine on an FPGA-based supercomputer, called Maxwell, developed at the University of Edinburgh. The latter consists of a 32 CPU cluster augmented with 64 Virtex-4 Xilinx FPGAs connected in a 2D torus. Our engine can implement various Monte-Carlo simulations on the Maxwell machine with speed-ups in the 3-order magnitude compared to equivalent software implementations. This is illustrated in this paper in the context of an implementation of the Black-Scholes option pricing model. Real hardware implementation shows that our FPGA-based implementation of the Black-Scholes model outperforms an equivalent software implementation running on a workstation cluster with the same number of computing nodes (CPU/FPGA) by a factor of 750, which is the fastest ever reported FPGA implementation of this model.