We follow up on the idea of Lars Arge to rephrase the Reduce and Apply operations of Binary Decision Diagrams (BDDs) as iterative I/O-efficient algorithms. We identify multiple avenues to simplify and improve the performance of his proposed algorithms. Furthermore, we extend the technique to other common BDD operations, many of which are not derivable using Apply operations alone. We provide asymptotic improvements to the few procedures that can be derived using Apply.Our work has culminated in a BDD package named Adiar that is able to efficiently manipulate BDDs that outgrow main memory. This makes Adiar surpass the limits of conventional BDD packages that use recursive depth-first algorithms. It is able to do so while still achieving a satisfactory performance compared to other BDD packages: Adiar, in parts using the disk, is on instances larger than 9.5 GiB only 1.47 to 3.69 times slower compared to CUDD and Sylvan, exclusively using main memory. Yet, Adiar is able to obtain this performance at a fraction of the main memory needed by conventional BDD packages to function.

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We follow up on the idea of Lars Arge to rephrase the Reduce and Apply algorithms of Binary Decision Diagrams (BDDs) as iterative I/Oefficient algorithms. We identify multiple avenues to improve the performance of his proposed algorithms and extend the technique to other basic BDD algorithms.These algorithms are implemented in a new BDD library, named Adiar. We see very promising results when comparing the performance of Adiar with conventional BDD libraries that use recursive depth-first algorithms. For instances of about 50 GiB, our algorithms, using external memory, are only up to 3.9 times slower compared to Sylvan, exclusively using internal memory. Yet, our proposed techniques are able to obtain this performance at a fraction of the internal memory needed by Sylvan to function. Furthermore, with Adiar we are able to manipulate BDDs that outgrow main memory and so surpass the limits of the other BDD libraries.