Single-chip multiprocessors and multiple-thread architectures are becoming an affordable solution for high-performance general-purpose workstations and servers.  On these machines, the workload is typically constituted of both sequential and parallel applications. Shared-bus shared-memory multithreaded multiprocessor can be used to speed-up the execution of such workload. In this environment, the scheduler takes care of the load balancing by allocating a ready process on the first available processor, thus producing process migration.  Process migration and the persistence of private data into different caches produce an undesired sharing, named passive sharing. The copies due to passive sharing produce useless coherence traffic on the bus and coping with such a problem may represent a challenging design problem for these machines.  Many protocols use smart solutions to limit the overhead to maintain coherence among shared copies. None of these studies treats passive-sharing directly, although some indirect effect is present while dealing with the other kinds of sharing. Affinity scheduling can alleviate this problem, but this technique does not adapt to all load conditions, especially when the effects of migration are massive.  A simple coherence protocol is presented. This protocol eliminates passive sharing using information from the compiler that is normally available in operating system kernels. The performance of this protocol has been evaluated and compared against other solutions proposed in the literature by means of enhanced trace-driven simulation.  The performance of the proposed solution outperforms the other protocols, especially in the case of a multithreaded processor, thus demonstrating its effectiveness in this kind of hardware platform.  The complexity of the proposed approach has been evaluated in terms of the number of protocol states, additional bus lines and required software support.  The protocol further limits the coherence-maintaining overhead by using information about access patterns to shared data exhibited in parallel applications.