Random number generators: good ones are hard to find
Communications of the ACM
Random number generators for parallel processors
Journal of Computational and Applied Mathematics - Random numbers and simulation
Implementing a random number package with splitting facilities
ACM Transactions on Mathematical Software (TOMS)
Computation of critical distances within multiplicative congruential pseudorandom number sequences
Journal of Computational and Applied Mathematics
Parallel Random Number Generation: Long-Range Correlations Among Multiple Processors
ParNum '99 Proceedings of the 4th International ACPC Conference Including Special Tracks on Parallel Numerics and Parallel Computing in Image Processing, Video Processing, and Multimedia: Parallel Computation
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Monte Carlo (MC) methods have proved to be flexible, robust and very useful techniques in computational finance. Several studies have investigated ways to achieve greater efficiency of such methods for serial computers. In this paper, we concentrate on the parallelization potentials of the MC methods. While MC is generally thought to be "embarrassingly parallel", the results eventually depend on the quality of the underlying parallel pseudo-random number generators. There are several methods for obtaining pseudo-random numbers on a parallel computer and we briefly present some alternatives. Then, we turn to an application of security pricing where we empirically investigate the pros and cons of the different generators. This also allows us to assess the potentials of parallel MC in the computational finance framework.