Finally, a number of investigators have effectively vectorized diverse Monte Carlo transport algorithms, with a sufficient base to establish a synthesized approach. We now even have a quantitative model which allows the assessment of the degree of parallelism and the amount of overhead required. Moreover, with the emergence of lagged Fibonacci generators, parallelization at any granularity appears to be easily implemented, and robust. Monte Carlo methods are widely used in modeling of materials and chemicals, from grain growth modeling in metallic alloys, to behavior of nanostrutures and polymers, and protein structure predictions. Monte Carlo in cancer treatment Glioblastoma multiforme is an especially virulent type of brain cancer that affects about 7,000 Americans each year.
Life expectancy is limited, with fewer than three percent surviving beyond five years.
It has been demonstrated that a typical BNCT neutron source engineering input deck could complete its calculations in 19.35 minutes by using 1,024 processors on the ORNL Intel Paragon XPS-150 massively parallel computer. This is a speed-up of over 350 times. Furthermore, it was demonstrated that the actual neutron transport calculations scaled linearly with increasing processors. These results may prove to be very significant in the broader use of the MCNP code, which had not been used for treatment calculations because it was not fast enough. Being able to do the calculations in under an hour would make it feasible to use MCNP for clinical irradiations.