On many-core processors that do not provide hard-ware cache coherence, using shared memory in parallel computations is challenging. Reverting to pure message passing would avoid consistency issues, but replicating large shared datasets by messages is less efficient than accessing them directly through shared memory. The TACO-MESH framework provides lightweight remote method calls and shared objects with software-managed consistency. This paper presents experience from porting a graph partitioning algorithm to the framework. A performance evaluation on the experimental Intel SCC processor, which has no hardware cache coherence, shows that parallelization can be efficient despite the overhead of software-level consistency management.