The success of all binary decision diagram (BDD) based synthesis and verification algorithms depend on the ability to efficiently manipulate very large BDDs. We present algorithms for manipulation of very large Binary Decision Diagrams (BDDs) on a network of workstations (NOW). A NOW provides a collection of main memories and disks which can be used effectively to create and manipulate very large BDDs. To make efficient use of memory resources of a Now, while completing execution in a reasonable amount of wall clock time, extension of breadth-first technique is used to manipulate BDDs. BDDs are partitioned such that nodes for a set of consecutive variables are assigned to the same workstation. We present experimental results to demonstrate the capability of such an approach and point towards the potential impact for manipulating very large BDDs.