Design and analysis of parallel Monte Carlo algorithms
SIAM Journal on Scientific and Statistical Computing - Papers from the Second Conference on Parallel Processing for Scientific Computin
Using linear congruential generators for parallel random number generation
WSC '89 Proceedings of the 21st conference on Winter simulation
Invited Lecture: PVM 3 Beyong Network Computing
Proceedings of the Second International ACPC Conference on Parallel Computation
| Hi-index | 0.00 |
One can construct the representation of solutions to various problems for parabolic differential equation with boundary conditions in the framework of classical stochastic analysis. This fact yields Monte Carlo methods for solving PDE's numerically. Instead of solving a PDE by common numeric techniques, one can simulate a stochastic system which allows for a simple parallelism with linear speed up. A number of numerical schemes exists for solving stochastic differential equations, i.e. the Euler scheme. If reflection is concerned, most methods have some shortcomings. In this article an efficient algorithm for simulating a reflected stochastic differential equation is developed. Results of numerical experiments are referenced revealing significant reduction in computational time gained by parallelization.