While coding the algorithm for this problem, we had to go through many iterations, first off we had to create it to run serially on the CPU. These results, while stable, were quite slow to process when it came to larger numbers. Next came the GPU port. This section was the bulk of the work over the three assignments as it required us to completely redesign how the program would function. We can say with confidence that based on the results of the port, it would not be worthwhile to even bother implementing the cuda code as the improvement was marginal at best. The third iteration however, when we optimized the code, showed a dramatic improvement in performance. Eight times faster than the GPU port from the second assignment, the optimization blew us away as to the effect that optimization can have on a program. The actual calculations on the arrays alone only ended up taking 35nsec. All in all, from the results collected, we can conclude that there is substantial evidence that parallelization of any form of Monte Carlo or repetitive program involving millions of small calculations would highly benefit from using the GPU.