Using model dataflow graphs to reduce the storage requirements of constraints

Abstract

Dataflow constraints allow programmers to easily specify relationships among application objects in a natural, declarative manner. Most constraint solvers represent these dataflow relationships as directed edges in a dataflow graph. Unfortunately, dataflow graphs require a great deal of storage. Consequently, an application with a large number of constraints can get pushed into virtual memory, and performance degrades in interactive applications. Our solution is based on the observation that objects derived from the same class use the same constraints, and thus have the same dataflow graphs. We represent the common dataflow patterns in a model dataflow graph that is stored with the class. Instance objects may derive explicit dependencies from this graph when the dependencies are needed. Model dependencies provide a useful new mechanism for improving the storage efficiency of dataflow constraint systems, especially when a large number of constrained objects must be managed.