Large production systems (rule-based systems) continue to suffer from extremely slow execution which limits their utility in practical applications as well as in research settings. Most investigations in speeding up these systems have focused on match (or knowledge-search) parallelism. Although gocd speed-ups have been achieved in this pnxzss, these investigations have revealed the limitations on the total speed-up available from this source. This limited speed-up is insufficient to alleviate the problem of slow execution in large-scale production system implementations. Such large-scale systems are expected to increase as researchers develop increasingly more competent production systems. In this paper, we focus on task-level parallelism, which is obtained by a high-level decomposition of the production system. Speed-ups obtained from task-level parallelism will multiply with the speed-ups obtained from match parallelism. The vehicle for our investigation of task-level parallelism is SPAM. a high-level vision system, implemented as a production system. SPAM is a mature research system with a typical run requiring between 50.000 to 400,000 production firings and an execution time of tbe order of 10 to 100 cpu hours. We report very encouraging speed-ups from task-level parallelism in SPAM - our parallel implementation shows near linear speed-ups of over 12 fold using 14 processors and points the way to substantial (SO-100 fold) speed-ups from task-level parallelism. We present a characterization of task-level parallelism in production systems and describe our methodology for selecting and applying a particular approach to parallel&e SPAM. Additionally, we report the speed-ups obtained from the use of shared virtual memory (network shared memory) in this implementation. Overall, task-level parallelism has not received much attention in the literature. Our experience illustrates that it is potentially a very important tool for speeding up large-scale production systems’.