Abstract:We study the problem of complexity estimation in the context of
parallelizing an advanced Branch and Bound-type algorithm over
graphical models. The algorithm's pruning power makes load balancing,
one crucial element of every distributed system, very challenging. We
propose using a statistical regression model to identify and tackle
disproportionally complex parallel subproblems, the cause of load
imbalance, ahead of time. The proposed model is evaluated and analyzed
on various levels and shown to yield robust predictions. We then
demonstrate its effectiveness for load balancing in practice.
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