On speeding up stochastic simulations by parallelization of random number generation
Che-Chi Shu1, Vu Tran1, Jeremy Binagia1
1School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, United States.
Chemical Engineering Science
|September 15, 2015
Summary
This study introduces a parallelized method for stochastic simulations, significantly reducing computational time for both Stochastic Simulation Algorithm (SSA) and Tau-leap methods. This approach enhances efficiency by parallelizing random number generation for multiple sample paths.
Area of Science:
- Computational Biology
- Biophysics
- Chemical Kinetics
Background:
- Stochastic simulations are crucial for modeling complex biological systems.
- Existing methods like SSA and Tau-leap algorithms compute sample paths sequentially.
- Computational efficiency remains a key challenge in these simulations.
Purpose of the Study:
- To develop a novel parallelized approach for stochastic simulations.
- To reduce computational time for Stochastic Simulation Algorithm (SSA) and Tau-leap methods.
- To enhance the efficiency of generating random numbers for sample path computation.
Main Methods:
- Parallelizing the generation of random subintervals for multiple sample paths.
- Applying the new strategy to both SSA and Tau-leap algorithms.
- Demonstrating the approach with various computational examples.
Main Results:
- Significant reduction in computational times for stochastic simulations.
- Demonstrated advantage across both SSA and Tau-leap algorithms.
- Reduced time for random number generator initiation.
Conclusions:
- The proposed parallelization strategy offers a substantial improvement in computational efficiency.
- This method is broadly applicable to existing stochastic simulation algorithms.
- The approach provides a valuable enhancement to the toolkit for computational modeling.
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