Research on programming distributed memory multiprocessors has resulted in a well-understood programming model, namely data-parallel programming. However, data-parallel programming in a multithreaded environment is far less understood. For example, if multiple threads within the same process belong to different data-parallel computations, then the architecture, compiler, or run-time system must ensure that relative indexing and collective operations are handled properly and efficiently. We introduce a run-time-based solution for data-parallel programming in a distributed memory environment that handles the problems of relative indexing and collective communications among thread groups. As a result, the data-parallel programming model can now be executed in a multithreaded environment, such as a system using threads to support both task and data parallelism.