Message Passing Interface Download

Message Passing Interface (MPI) is a standardized and portable message-passing standard designed to function on parallel computing architectures.[1] The MPI standard defines the syntax and semantics of library routines that are useful to a wide range of users writing portable message-passing programs in C, C++, and Fortran. There are several open-source MPI implementations, which fostered the development of a parallel software industry, and encouraged development of portable and scalable large-scale parallel applications.

MPI provides parallel hardware vendors with a clearly defined base set of routines that can be efficiently implemented. As a result, hardware vendors can build upon this collection of standard low-level routines to create higher-level routines for the distributed-memory communication environment supplied with their parallel machines. MPI provides a simple-to-use portable interface for the basic user, yet one powerful enough to allow programmers to use the high-performance message passing operations available on advanced machines.

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In an effort to create a universal standard for message passing, researchers did not base it off of a single system but it incorporated the most useful features of several systems, including those designed by IBM, Intel, nCUBE, PVM, Express, P4 and PARMACS. The message-passing paradigm is attractive because of wide portability and can be used in communication for distributed-memory and shared-memory multiprocessors, networks of workstations, and a combination of these elements. The paradigm can apply in multiple settings, independent of network speed or memory architecture.

MPI is a communication protocol for programming[4] parallel computers. Both point-to-point and collective communication are supported. MPI "is a message-passing application programmer interface, together with protocol and semantic specifications for how its features must behave in any implementation."[5] MPI's goals are high performance, scalability, and portability. MPI remains the dominant model used in high-performance computing today.[6]

The principal MPI-1 model has no shared memory concept, and MPI-2 has only a limited distributed shared memory concept. Nonetheless, MPI programs are regularly run on shared memory computers, and both MPICH and Open MPI can use shared memory for message transfer if it is available.[7][8] Designing programs around the MPI model (contrary to explicit shared memory models) has advantages when running on NUMA architectures since MPI encourages memory locality. Explicit shared memory programming was introduced in MPI-3.[9][10][11]

Most MPI implementations consist of a specific set of routines directly callable from C, C++, Fortran (i.e., an API) and any language able to interface with such libraries, including C#, Java or Python. The advantages of MPI over older message passing libraries are portability (because MPI has been implemented for almost every distributed memory architecture) and speed (because each implementation is in principle optimized for the hardware on which it runs).

At present, the standard has several versions: version 1.3 (commonly abbreviated MPI-1), which emphasizes message passing and has a static runtime environment, MPI-2.2 (MPI-2), which includes new features such as parallel I/O, dynamic process management and remote memory operations,[14] and MPI-3.1 (MPI-3), which includes extensions to the collective operations with non-blocking versions and extensions to the one-sided operations.[15]MPI-2's LIS specifies over 500 functions and provides language bindings for ISO C, ISO C++, and Fortran 90. Object interoperability was also added to allow easier mixed-language message passing programming. A side-effect of standardizing MPI-2, completed in 1996, was clarifying the MPI-1 standard, creating the MPI-1.2.

MPI is often compared with Parallel Virtual Machine (PVM), which is a popular distributed environment and message passing system developed in 1989, and which was one of the systems that motivated the need for standard parallel message passing. Threaded shared memory programming models (such as Pthreads and OpenMP) and message passing programming (MPI/PVM) can be considered complementary and have been used together on occasion in, for example, servers with multiple large shared-memory nodes.

The MPI interface is meant to provide essential virtual topology, synchronization, and communication functionality between a set of processes (that have been mapped to nodes/servers/computer instances) in a language-independent way, with language-specific syntax (bindings), plus a few language-specific features. MPI programs always work with processes, but programmers commonly refer to the processes as processors. Typically, for maximum performance, each CPU (or core in a multi-core machine) will be assigned just a single process. This assignment happens at runtime through the agent that starts the MPI program, normally called mpirun or mpiexec.

MPI-1 and MPI-2 both enable implementations that overlap communication and computation, but practice and theory differ. MPI also specifies thread safe interfaces, which have cohesion and coupling strategies that help avoid hidden state within the interface. It is relatively easy to write multithreaded point-to-point MPI code, and some implementations support such code. Multithreaded collective communication is best accomplished with multiple copies of Communicators, as described below.

Passing a data structure as one block is significantly faster than passing one item at a time, especially if the operation is to be repeated. This is because fixed-size blocks do not require serialization during transfer.[19]

The key aspect is "the ability of an MPI process to participate in the creation of new MPI processes or to establish communication with MPI processes that have been started separately." The MPI-2 specification describes three main interfaces by which MPI processes can dynamically establish communications, MPI_Comm_spawn, MPI_Comm_accept/MPI_Comm_connect and MPI_Comm_join. The MPI_Comm_spawn interface allows an MPI process to spawn a number of instances of the named MPI process. The newly spawned set of MPI processes form a new MPI_COMM_WORLD intracommunicator but can communicate with the parent and the intercommunicator the function returns. MPI_Comm_spawn_multiple is an alternate interface that allows the different instances spawned to be different binaries with different arguments.[20]

While the specifications mandate a C and Fortran interface, the language used to implement MPI is not constrained to match the language or languages it seeks to support at runtime. Most implementations combine C, C++ and assembly language, and target C, C++, and Fortran programmers. Bindings are available for many other languages, including Perl, Python, R, Ruby, Java, and CL (see #Language bindings).

The ABI of MPI implementations are roughly split between MPICH and Open MPI derivatives, so that a library from one family works as a drop-in replacement of one from the same family, but direct replacement across families is impossible. The French CEA maintains a wrapper interface to facilitate such switches.[27]

Another approach has been to add hardware acceleration to one or more parts of the operation, including hardware processing of MPI queues and using RDMA to directly transfer data between memory and the network interface controller without CPU or OS kernel intervention.

However, this original project also defined the mpiJava API[32] (a de facto MPI API for Java that closely followed the equivalent C++ bindings) which other subsequent Java MPI projects adopted. One less-used API is MPJ API, which was designed to be more object-oriented and closer to Sun Microsystems' coding conventions.[33] Beyond the API, Java MPI libraries can be either dependent on a local MPI library, or implement the message passing functions in Java, while some like P2P-MPI also provide peer-to-peer functionality and allow mixed-platform operation.

Another Java message passing system is MPJ Express.[34] Recent versions can be executed in cluster and multicore configurations. In the cluster configuration, it can execute parallel Java applications on clusters and clouds. Here Java sockets or specialized I/O interconnects like Myrinet can support messaging between MPJ Express processes. It can also utilize native C implementation of MPI using its native device. In the multicore configuration, a parallel Java application is executed on multicore processors. In this mode, MPJ Express processes are represented by Java threads.

Here is a "Hello, World!" program in MPI written in C. In this example, we send a "hello" message to each processor, manipulate it trivially, return the results to the main process, and print the messages.

MPI isn't endorsed as an official standard by any standards organization, such as the Institute of Electrical and Electronics Engineers (IEEE) or the International Organization for Standardization (ISO), but it's generally considered to be the industry standard, and it forms the basis for most communication interfaces adopted by parallel computing programmers. Various implementations of MPI have been created by developers as well.

A small group of researchers in Austria began discussing the concept of a message passing interface in 1991. A Workshop on Standards for Message Passing in a Distributed Memory Environment, sponsored by the Center for Research on Parallel Computing, was held a year later in Williamsburg, Va. A working group was established to create the standardization process.

The older MPI 1.3 standard, dubbed MPI-1, provides over 115 functions. The later MPI 2.2 standard, or MPI-2, offers over 500 functions and is largely backward compatible with MPI-1. However, not all MPI libraries provide a full implementation of MPI-2. MPI-2 included new parallel I/O, dynamic process management as well as remote memory operations. The MPI-3 standard released in November 2014 improves scalability, enhances performance, includes multicore and cluster support and interoperates with more applications. In 2021, The MPI Forum released MPI 4.0. It introduced partitioned communications, a new tool interface, Persistent Collectives and other new additions. MPI 5.0 is currently under development. 006ab0faaa