MPI (Message Passing Interface)

MPI (Message Passing Interface)
Name	Message Passing Interface
Developer	High Performance Computing, Argonne National Laboratory, Lawrence Livermore National Laboratory, Los Alamos National Laboratory
Released	1994
Programming language	C (programming language), Fortran
Operating system	Unix, Linux, Windows
License	Various

MPI (Message Passing Interface) is a standardized and portable message-passing system designed to function on a wide variety of parallel computing architectures. It provides a specification for communication among processes that enables implementations to interoperate across different hardware produced by organizations such as Intel Corporation, Cray Research, IBM, and Sun Microsystems. MPI is central to high-performance computing environments at institutions including Oak Ridge National Laboratory, Argonne National Laboratory, and Lawrence Berkeley National Laboratory.

Overview

MPI defines a suite of library routines for point-to-point and collective communication used in parallel applications executed on clusters like those at National Energy Research Scientific Computing Center, supercomputers such as Summit (supercomputer), Fugaku, and distributed systems from vendors including Hewlett-Packard and Dell Technologies. Major research centers such as CERN, NASA Ames Research Center, and Los Alamos National Laboratory use MPI in simulations alongside tools like OpenMP, CUDA, and OpenCL. Standards committees and consortia including MPI Forum, IEEE, and national labs coordinate development and adoption across projects like TOP500 and Blue Gene.

History and Development

MPI originated from collaborative efforts involving groups at Argonne National Laboratory, Lawrence Livermore National Laboratory, and University of Tennessee. Early design discussions involved contributors from Intel Corporation, IBM, and Cray Research responding to needs expressed at conferences such as Supercomputing Conference and workshops sponsored by Department of Energy (United States). The first MPI standard was ratified in 1994 with subsequent revisions guided by the MPI Forum and stakeholders from research centers like Oak Ridge National Laboratory and universities including Massachusetts Institute of Technology and Stanford University. Influential projects and personnel from NASA, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Argonne National Laboratory shaped features adopted by later revisions and by implementations from vendors such as IBM and Cray Research.

Design and Features

The MPI specification includes point-to-point primitives modeled after work at University of California, Berkeley, nonblocking semantics advanced by researchers at University of Illinois Urbana-Champaign, and collective operations inspired by algorithms from Bell Labs and AT&T Corporation. It defines communicators and datatypes that facilitate heterogeneous system interoperability across platforms from Sun Microsystems and Silicon Graphics. Robustness features reflect input from standards organizations like IEEE and research institutions such as Rensselaer Polytechnic Institute and University of Cambridge. The design supports fault-tolerance experiments pursued at Los Alamos National Laboratory, resource management interoperable with schedulers like SLURM and PBS (software), and performance profiling via tools from NVIDIA and Intel Corporation.

Implementations and Libraries

Multiple implementations implement the MPI standard, including open-source projects sponsored by organizations such as Open MPI Project, MPICH (originating at Argonne National Laboratory), and vendor implementations from IBM for Blue Gene, Cray Research for Cray systems, and Intel Corporation. Research prototypes and production stacks are used by institutions like CERN, Oak Ridge National Laboratory, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and universities including University of Oxford and University of Cambridge. Libraries interoperate with middleware from Apache Software Foundation projects, storage systems from NetApp, and network fabrics from Mellanox Technologies and Broadcom Inc..

Programming Model and APIs

MPI exposes language bindings for C (programming language), Fortran, and interfaces used in projects at Massachusetts Institute of Technology, Stanford University, and University of California, Berkeley. The API encompasses routines for point-to-point messaging, collective communication, group management, and one-sided communication developed through collaborations involving Argonne National Laboratory and Lawrence Livermore National Laboratory. Integration efforts connect MPI with programming models like OpenMP, tasking systems studied at ETH Zurich, and accelerator frameworks used by NVIDIA and AMD (company). Educational materials from Courant Institute of Mathematical Sciences and courses at MIT and Stanford University teach MPI APIs alongside debugging tools from TotalView and Intel Debugger.

Performance and Optimization

Performance engineering for MPI-based applications is an active area at centers such as Oak Ridge National Laboratory, Argonne National Laboratory, and Lawrence Berkeley National Laboratory. Optimizations include network offload techniques implemented on fabrics by Mellanox Technologies and Intel Corporation, topology-aware collectives researched at University of Illinois Urbana-Champaign and Universidad Politécnica de Madrid, and tuning strategies used in benchmarks like those on the TOP500 list. Profiling and tracing tools originate from projects at National Institute of Standards and Technology, Lawrence Livermore National Laboratory, and commercial vendors like Intel and NVIDIA to measure latency, bandwidth, and scalability on systems such as Fugaku and Summit (supercomputer).

Applications and Use Cases

MPI is employed in scientific computing workflows at CERN for particle physics simulations, climate modeling at NOAA, computational chemistry at Lawrence Berkeley National Laboratory, and astrophysics at NASA Ames Research Center. Engineering firms and institutions such as Boeing, Lockheed Martin, and General Electric use MPI in finite-element and computational fluid dynamics codes developed at Stanford University and Massachusetts Institute of Technology. Bioinformatics pipelines at Broad Institute and Wellcome Sanger Institute, financial risk simulations at Goldman Sachs and JP Morgan Chase, and data analytics clusters in enterprises like Amazon (company) and Google integrate MPI with other stacks including Hadoop and Spark.

Category:Computer networking