The importance of production systems in artificial intelligence has been repeatedly demonstrated by a number of expert systems. Much effort has therefore been expended on finding an efficient processing mechanism for processing production systems. Variable resolution actors, called macroactors, a processing mechanism for production systems, are studied. A macrotoken, which is a collection of primitive data tokens, is introduced as a companion to macroactors. An approach to obtaining medium-grain parallelism, called macroactor/token, is investigated. A set of guidelines is identified in the context of production systems to derive well-formed macroactors from primitive macroactors. Parallel pattern matching is written in macro actors/tokens to be executed on a macro-data-flow simulator. Simulation results demonstrate that the macro approach can be an efficient implementation of the production system paradigm.