Lessons learned about one-way, dataflow constraints in the Garnet and Amulet graphical toolkits

Abstract

One-way, dataflow constraints are commonly used in graphical interface toolkits, programming environments, and circuit applications. Previous papers on dataflow constraints have focused on the design and implementation of individual algorithms. In contrast, this article focuses on the lessons we have learned from a decade of implementing competing algorithms in the Garnet and Amulet graphical interface toolkits. These lessons reveal the design and implementation tradeoffs for different one-way, constraint satisfaction algorithms. The most important lessons we have learned are that (1) mark-sweep algorithms are more efficient than topological ordering algorithms; (2) lazy and eager evaluators deliver roughly comparable performance for most applications; and (3) constraint satisfaction algorithms have more than adequate speed, except that the storage required by these algorithms can be problematic.