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The BDD package Adiar manipulates Binary Decision Diagrams (BDDs) in externalmemory. This enables handling big BDDs, but the performance suffers whendealing with moderate-sized BDDs. This is mostly due to initializing expensiveexternal memory data structures, even if their contents can fit entirely insideinternal memory.  The contents of these auxiliary data structures always correspond to a graphcut in an input or output BDD. Specifically, these cuts respect the levels ofthe BDD. We formalise the shape of these cuts and prove sound upper bounds ontheir maximum size for each BDD operation.  We have implemented these upper bounds within Adiar. With these bounds, itcan predict whether a faster internal memory variant of the auxiliary datastructures can be used. In practice, this improves Adiar's running time acrossthe board. Specifically for the moderate-sized BDDs, this results in an averagereduction of the computation time by 86.1% (median of 89.7%). In some cases,the difference is even 99.9\%. When checking equivalence of hardware circuitsfrom the EPFL Benchmark Suite, for one of the instances the time was decreasedby 52 hours.

Predicting Memory Demands of BDD Operations using Maximum Graph Cuts (Extended Paper)