Fast Simulation via Precomputed Sample Repository
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Solution Overview
Problem
Existing simulation methods, such as Monte Carlo simulations, require extensive computational resources and time to achieve accurate results due to the need for large numbers of sample calculations, making real-time processing challenging, especially in applications like derivative pricing where calculations can span weeks or months.
Innovation Solution
A fast simulation approach that utilizes precomputed simulation results stored in a sample repository, allowing for the selection of a smaller subset of samples based on statistical distribution and distance analysis, enabling real-time computation by retrieving and combining precomputed results for test parameter vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Monte Carlo simulation methods are used to ensure accurate results through large numbers of sample calculations, then measurement precision is improved, but productivity deteriorates due to extensive computational time and resources
Solution Approach 1:
The patent precomputes simulation results for multiple parameter sets before actual use. A sample repository is built in advance containing precomputed simulation outcomes for various parameter combinations. During runtime, the system retrieves and combines relevant precomputed results rather than performing full simulations, dramatically reducing computational time while maintaining accuracy through the use of pre-established sample data.
2Measurement precision
If a large number of samples are used in simulation computations, then measurement precision is improved, but loss of time increases due to the extensive processing required
Solution Approach 1:
The patent extracts and stores simulation results in a sample repository for later retrieval. Instead of computing all samples during execution, the system extracts only the necessary precomputed results from the repository based on the specific parameter vectors needed, combining them to produce accurate simulation outcomes with minimal computational time.
3Measurement precision
If extensive computational resources are allocated to achieve accurate simulation results, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs computationally intensive operations in advance by precomputing simulation results for multiple parameter sets and storing them in a sample repository. This shifts the computational burden from runtime to setup phase, allowing the actual simulation execution to use minimal computational resources while maintaining high accuracy through retrieval and combination of preestablished samples.
Data Source
AI summary
Systems and methods are provided for performing a fast simulation using test parameter vectors as inputs. The method includes retrieving precomputed samples from a sample repository stored in a non-volatile memory, the precomputed samples being precomputed using a simulated model, predetermined parameter vectors, and random inputs; storing respective subsets of the precomputed samples in local memories of a plurality of respective hardware processors; storing the test parameter vectors in the local memories of the hardware processors; at each of the hardware processors, selecting a subset of the precomputed samples stored in the local memory of the hardware processor based on the test parameter vectors, computing test samples by executing the simulated model using the test parameter vectors and the random inputs; and combining the subset of the precomputed samples and the test samples to produce a simulation result.