Probabilistic Approach to Enable Extreme-Scale Simulations under Uncertainty and System Faults. Final Technical Report
The current project develops a novel approach that uses a probabilistic description to capture the current state of knowledge about the computational solution. To effectively spread the computational effort over multiple nodes, the global computational domain is split into many subdomains. Computational uncertainty in the solution translates into uncertain boundary conditions for the equation system to be solved on those subdomains, and many independent, concurrent subdomain simulations are used to account for this bound- ary condition uncertainty. By relying on the fact that solutions on neighboring subdomains must agree with each other, a more accurate estimate for the global solution can be achieved. Statistical approaches in this update process make it possible to account for the effect of system faults in the probabilistic description of the computational solution, and the associated uncertainty is reduced through successive iterations. By combining all of these elements, the probabilistic reformulation allows splitting the computational work over very many independent tasks for good scalability, while being robust to system faults.