11.2 Message-passing interface

The Message-Passing Interface (MPI) was designed to be an industrial-strength message-passing environment that is portable across a wide range of hardware environments.

Much like High Performance FORTRAN, MPI was developed by a group of computer vendors, application developers, and computer scientists. The idea was to come up with a specification that would take the strengths of many of the existing proprietary message passing environments on a wide variety of architectures and come up with a specification that could be implemented on architectures ranging from SIMD systems with thousands of small processors to MIMD networks of workstations and everything in between.

Interestingly, the MPI effort was completed a year after the High Performance FORTRAN (HPF) effort was completed. Some viewed MPI as a portable message-passing interface that could support a good HPF compiler. Having MPI makes the compiler more portable. Also having the compiler use MPI as its message-passing environment insures that MPI is heavily tested and that sufficient resources are invested into the MPI implementation.

Pvm versus mpi

While many of the folks involved in PVM participated in the MPI effort, MPI is not simply a follow-on to PVM. PVM was developed in a university/research lab environment and evolved over time as new features were needed. For example, the group capability was not designed into PVM at a fundamental level. Some of the underlying assumptions of PVM were based “on a network of workstations connected via Ethernet” model and didn’t export well to scalable computers. One should not diminish the positive contributions of PVM, however. PVM was the first widely avail- able portable message-passing environment. PVM pioneered the idea of heterogeneous distributed computing with built-in format conversion. In some ways, MPI is more robust than PVM, and in other ways, MPI is simpler than PVM. MPI doesn’t specify the system management details as in PVM; MPI doesn’t specify how a virtual machine is to be created, operated, and used.

Mpi features

MPI has a number of useful features beyond the basic send and receive capabilities. These include:

• Communicators : A communicator is a subset of the active processes that can be treated as a group for collective operations such as broadcast, reduction, barriers, sending, or receiving. Within each communicator, a process has a rank that ranges from zero to the size of the group. A process may be a member of more than one communicator and have a different rank within each communicator. There is a default communicator that refers to all the MPI processes that is called MPI_COMM_WORLD .
• Topologies : A communicator can have a topology associated with it. This arranges the processes that belong to a communicator into some layout. The most common layout is a Cartesian decomposition. For example, 12 processes may be arranged into a 3×4 grid. Sounds a little like HPF, no? Once these topologies are defined, they can be queried to find the neighboring processes in the topology. In addition to the Cartesian (grid) topology, MPI also supports a graph-based topology.
• Communication modes : MPI supports multiple styles of communication, including blocking and non- blocking. Users can also choose to use explicit buffers for sending or allow MPI to manage the buffers. The nonblocking capabilities allow the overlap of communication and computation. MPI can support a model in which there is no available memory space for buffers and the data must be copied directly from the address space of the sending process to the memory space of the receiving process. MPI also supports a single call to perform a send and receive that is quite useful when processes need to exchange data.
• Single-call collective operations : Some of the calls in MPI automate collective operations in a single call. For example, the broadcast operation sends values from the master to the slaves and receives the values on the slaves in the same operation. The net result is that the values are updated on all processes. Similarly, there is a single call to sum a value across all of the processes to a single value. By bundling all this functionality into a single call, systems that have support for collective operations in hardware can make best use of this hardware. Also, when MPI is operating on a shared-memory environment, the broadcast can be simplified as all the slaves simply make a local copy of a shared variable.