MPIDs

MPIDs are identifiers used in distributed computing to uniquely label processes, messages or communication contexts within the Message Passing Interface (MPI) framework. The MPI specification does not provide a single field named "MPID", but various MPI implementations and debugging tools assign numeric or alphanumeric IDs to each process (commonly called ranks), to each communicator, and to each message sent across a communicator. These IDs facilitate tracking, debugging, and performance analysis of parallel applications.

In an MPI program, the routine MPI_Comm_rank returns an integer that serves as a process MPID within the communicator it is called on. Communicators themselves are often represented by opaque handles that can be queried for unique IDs. Moreover, message tags, when combined with source and destination ranks, implicitly define a tuple that uniquely identifies a particular message exchange. MPI profiling interfaces, such as PMPI, allow collection of MPIDs in the form of exportable trace files (e.g., in PAIR or PADS formats) where each entry records the sender and receiver MPIDs along with other metadata.

MPIDs become especially useful when using performance measurement tools like Vampir, Extra-C, or TAU, which generate trace files and require numeric or symbolically resolved IDs to reconstruct the communication graph of an application. Many MPI debuggers, such as TotalView or DDT, display MPIDs to help developers locate deadlocks or race conditions.

Because the MPI standard itself does not mandate a global MPID convention, implementations and tooling often define their own mapping rules. Users typically rely on the MPI runtime to assign monotonically increasing integers to processes in the order in which they were spawned. This simple numeric scheme ensures that MPIDs are stable across runs on the same topology, aiding reproducibility and autocorrelation of output.

In summary, MPIDs are the numeric or symbolic handles that identify MPI processes, communicators, and messages. They underpin many debugging and performance‑analysis workflows in high‑performance computing and are essential for understanding and optimizing parallel applications.