Workflow of Fast Monte Carlo Method
The Fast Monte Carlo (FMC) method first runs a group of samples and obtains measurements, then the algorithm builds a model and predicts the worst sample based on the user-specified results and specifications. It may take a few group iterations from model building, worst-sampling prediction, and verification to find out the tail of distribution. The FMC method thus improves performance and reduces memory cost when compared with the normal Monte Carlo method.
The FMC method also supports multi-process distribution to further improve the performance. The sample generation, model evaluation, and simulation are processed by worker processes running in parallel.
The following figure shows the workflow of the FMC method.
Related Topics
Running the Fast Monte Carlo Method
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