In global adaptivity, it is very often the case that there exists only a particular region of interest on the macro scale, which leads to only micro simulations in that region to be active. From a performance perspective this is highly inefficient, because it means that some processors solve a large number of micro simulations, while other processors are idle. In a recent study where we scaled the two-scale-heat-conduction case to have 128 micro simulations, we saw the effect of the load imbalance:
A dynamic load balancing technique which would redistribute the micro simulations across processors would aide to increased performance and scalability.
In global adaptivity, it is very often the case that there exists only a particular region of interest on the macro scale, which leads to only micro simulations in that region to be active. From a performance perspective this is highly inefficient, because it means that some processors solve a large number of micro simulations, while other processors are idle. In a recent study where we scaled the two-scale-heat-conduction case to have 128 micro simulations, we saw the effect of the load imbalance:
A dynamic load balancing technique which would redistribute the micro simulations across processors would aide to increased performance and scalability.