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MPI_Recv
MPI_Recv is a blocking message-passing routine in the Message Passing Interface (MPI) standard used for receiving messages in distributed-memory parallel programs. It is central to point-to-point communication in MPI implementations and is used across high-performance computing applications on systems developed by organizations such as Cray Research, IBM, Intel Corporation, NVIDIA, and deployed at facilities like Oak Ridge National Laboratory, Lawrence Livermore National Laboratory, and Argonne National Laboratory. The routine's semantics and usage are specified in the MPI standard maintained by the MPI Forum and are implemented in libraries such as Open MPI, MPICH, Intel MPI Library, and MVAPICH2.
MPI_Recv receives a message sent by a matching send operation and places the incoming data into a user-supplied buffer. The call appears in the MPI standard alongside routines like MPI_Send, MPI_Isend, MPI_Irecv, MPI_Wait, and MPI_Test, forming the core of MPI point-to-point communication patterns used in applications developed by teams at institutions such as Lawrence Berkeley National Laboratory, Los Alamos National Laboratory, and universities including Massachusetts Institute of Technology, Stanford University, and University of Cambridge. MPI_Recv is commonly used in producer-consumer patterns, master-worker topologies, and irregular graph algorithms applied in projects like Lustre (file system), PETSc, and Trilinos.
The MPI_Recv interface accepts parameters specifying the receive buffer, count, datatype, source rank, tag, communicator, and a status object. Implementations in C, Fortran, and language bindings by vendors such as Microsoft Research and projects like HPX adhere to the parameter ordering and types defined by the MPI Forum specification. The status object returned by MPI_Recv contains metadata including the source and tag and is used with routines such as MPI_Get_count and MPI_Test_cancelled. Typical parameters reference communicators like MPI_COMM_WORLD and constants such as MPI_ANY_SOURCE and MPI_ANY_TAG, tying into initialization via MPI_Init and finalization via MPI_Finalize.
MPI_Recv is defined as a blocking receive: it does not return until the receive buffer contains the incoming data and the associated status is available. This blocking semantics interacts with progress rules and rendezvous protocols described by the MPI Forum and implemented by vendors like Cray Inc. and Intel Corporation. Blocking behavior influences deadlock scenarios in classical examples such as the two-process send-receive deadlock studied by researchers from University of Illinois Urbana–Champaign and in parallel patterns used at centers like European Centre for Medium-Range Weather Forecasts and National Center for Atmospheric Research.
MPI_Recv returns an error code indicating success or a failure class; implementations map these to human-readable diagnostics used by tools from Sandia National Laboratories and vendors like ARM Holdings and Hewlett Packard Enterprise. Common error classes include communicator-related errors, datatype mismatches, and tag/source mismatches; these are defined by the MPI Forum and are surfaced in debuggers such as TotalView and Allinea DDT. Robust error handling patterns often combine MPI error handlers like MPI_ERRORS_RETURN with logging frameworks used in consortium projects at Oak Ridge National Laboratory and Argonne National Laboratory.
MPI_Recv is used in idioms such as simple blocking receive loops, receive-then-process workflows, and combined with nonblocking operations like MPI_Isend to implement overlap of communication and computation in applications by teams at Los Alamos National Laboratory and Sandia National Laboratories. Typical patterns include master-worker using MPI_Comm_rank and MPI_Comm_size to distribute tasks, and pipeline topologies found in workflows at CERN and in climate models developed at Met Office. Debugging and correctness checks often use MPI_Barrier and status inspection via MPI_Status methods.
Performance of MPI_Recv depends on network hardware such as InfiniBand, Intel Omni-Path, and vendor NICs by Mellanox Technologies and Broadcom, as well as software layers in libraries like libfabric and implementations by Open MPI and MPICH. Factors include eager versus rendezvous protocols, message size thresholds tuned by vendors like NVIDIA and Intel Corporation, and memory registration costs that matter on systems at Argonne National Laboratory and Oak Ridge National Laboratory. Profiling with tools from Periscope and TAU Performance System and tuning with job schedulers like Slurm Workload Manager can reveal bottlenecks when using blocking receives in large-scale workflows on systems managed by institutions such as Lawrence Livermore National Laboratory.
MPI_Recv is bound in languages with standardized interfaces, including C, Fortran, and third-party bindings for Python via wrappers like mpi4py, and integrations with frameworks developed at Google and Facebook for internal distributed systems research. Native implementations are provided by projects such as Open MPI, MPICH, Intel MPI Library, and MVAPICH2, and optimized for hardware platforms by vendors including NVIDIA and Cray Inc.. Bindings and interoperability with accelerators such as CUDA and tools from Khronos Group are areas of active integration and research at institutions like Lawrence Berkeley National Laboratory and industry teams at AMD.