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Abstract

Monte-Carlo (MC) simulation is an effective tool for solving complex problems such as many-body simulation, exotic option pricing and partial differential equation solving. The huge amount of computation in MC makes it a good candidate for acceleration using hardware and distributed computing platforms. We propose a novel MC simulation framework suitable for a wide range of problems. This framework enables different hardware accelerators in a multi-accelerator heterogeneous cluster to work collaboratively on a single application. It also provides scheduling interfaces to adaptively balance the workload according to the cluster status. Two financial applications, involving asset simulation and option pricing, are built using this framework to demonstrate its capability and flexibility. A cluster with 8 Virtex-5 xc5vlx330t FPGAs and 8 Tesla C1060 GPUs using the proposed framework provides 44 times speedup and 19.6 times improved energy efficiency over a cluster with 16 AMD Phenom 9650 quad-core 2.4GHz CPUs for the GARCH asset simulation application. The Efficient Allocation Line (EAL) is proposed for determining the most efficient allocation of accelerators for either performance or energy consumption.

Arithmetic Asian options are financial derivatives which have the feature of path-dependency: they depend on the entire price path of the underlying asset, rather than just the instantaneous price. This path-dependency makes them difficult to price, as only computationally intensive Monte-Carlo methods can provide accurate prices. This paper proposes an FPGA-accelerated Asian option pricing solution, using a highly-optimised parallel Monte-Carlo architecture. The proposed pipelined design is described parametrically, facilitating its re-use for different technologies. An implementation of this architecture in a Virtex-5 xc5vlx330t FPGA at 200MHz is 313 times faster than a multi-threaded software implementation running on a Intel Xeon E5420 quad-core CPU at 2.5GHz; it is also 2.2 times faster than the Tesla C1060 GPU at 1.3 GHz.