Visual analytics for stochastic simulation in cell biology
i-KNOW '11 Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
Using workflows to control the experiment execution in modeling and simulation software
Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques
JAMES II: extending, using, and experiments
Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques
Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques
Panel on grand challenges for modeling and simulation
Proceedings of the Winter Simulation Conference
Streaming data management for the online processing of simulation data
Proceedings of the Winter Simulation Conference
SESSL: A domain-specific language for simulation experiments
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Hi-index | 0.00 |
Current and upcoming architectures of desktop and high performance computers offer increasing means for parallel execution. Since the computational demands induced by ever more realistic models increase steadily, this trend is of growing importance for systems biology. Simulations of these models may involve the consideration of multiple parameter combinations, their replications, data collection, and data analysis - all of which offer different opportunities for parallelization. We present a brief theoretical analysis of these opportunities in order to show their potential impact on the overall computation time. The benefits of using more than one opportunity for parallelization are illustrated by a set of benchmark experiments, which furthermore show that parallelizability should be exploited in a flexible manner to achieve speedup.