Hierarchical dynamic workload scheduling on heterogeneous clusters for grid search of inverse problems

Inverse problems occur in many scientific fields. Albeit grid search, where points of a regular grid are tested as possible solutions, is a straightforward and robust method to numerically solve inverse problems, it is computationally intensive and becomes prohibitive when the problem has a high dimensionality. Heterogeneous clusters are a viable and cost-effective solution to exploit the combined computational power of multiple available computers. In this paper, we present a computing framework that supports efficient grid search for inverse problems on heterogeneous clusters. Scheduling the workload on such systems might be challenging, especially when nodes are comprised of CPUs and GPUs with different computational speeds. The framework dynamically schedules computations on the processing elements of the cluster according to a selected performance index, which is determined at run-time. The framework is extensible, as it allows easy integration of additional inverse problems.