High performance computing is critical for financial
markets where analysts seek to accelerate complex
optimizations such as pricing engines to maintain a
competitive edge. In this paper we investigate the performance
of financial workloads on the Sony-Toshiba-
IBM Cell Broadband Engine, a heterogeneous multicore
chip architected for intensive gaming applications and
high performance computing. We analyze the use of
Monte Carlo techniques for financial workloads and design
efficient parallel implementations of different high
performance pseudo and quasi random number generators
as well as normalization techniques. Our implementation
of the Mersenne Twister pseudo random
number generator outperforms current Intel and AMD
architectures by over an order of magnitude. Using
these new routines, we optimize European Option (EO)
and Collateralized Debt Obligation (CDO) pricing algorithms.
Our Cell-optimized EO pricing achieves a
speedup of over 2 in comparison with using RapidMind
SDK for Cell, and comparing with GPU, a speedup of
1.26 as compared with using RapidMind SDK for GPU
(NVIDIA GeForce 8800), and a speedup of 1.51 over
NVIDIA GeForce 8800 (using CUDA). Our detailed
analyses and performance results demonstrate that the
Cell/B.E. processor is well suited for financial workloads
and Monte Carlo simulation.
