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Flynn's taxonomy is a classification of computer architectures, proposed by Michael J. Flynn in 1966.^[1][2] The four classifications defined by Flynn are based upon the number of concurrent instruction (or control) and data streams available in the architecture:

• Single Instruction, Single Data stream (SISD) - a sequential computer which exploits no parallelism in either the instruction or data streams. Examples of SISD architecture are the traditional uniprocessor machines like a PC or old mainframes.
• Multiple Instruction, Single Data stream (MISD) - unusual due to the fact that multiple instruction streams generally require multiple data streams to be effective. However, this type is used when it comes to redundant parallelism, as for example on airplanes that need to have several backup systems in case one fails. Some theoretical computer architectures have also been proposed which make use of MISD, but none have entered mass production.
• Single Instruction, Multiple Data streams (SIMD) - a computer which exploits multiple data streams against a single instruction stream to perform operations which may be naturally parallelised. For example, an array processor or GPU.
• Multiple Instruction, Multiple Data streams (MIMD) - multiple autonomous processors simultaneously executing different instructions on different data. Distributed systems are generally recognised to be MIMD architectures; either exploiting a single shared memory space or a distributed memory space.

As of 2006, all the top 10 and most of the TOP500 supercomputers are based on a MIMD architecture.

Some people[1][2][3][4][5] further divide the MIMD category into:

• Single Program, Multiple Data streams (SPMD) - multiple autonomous processors simultaneously executing the same program (but at independent points, rather than in the lockstep that SIMD imposes) on different data. Also referred to as 'Single Process, multiple data'[6]. SPMD is the most common style of parallel programming[7].
• Multiple Program Multiple Data (MPMD) -- multiple autonomous processors simultanously operating at least 2 independent programs. Typically such systems pick one node to be the "host" ("the explicit host/node programming model") or "manager" (the "Manager/Worker" strategy), which runs one program that farms out data to all the other nodes which all run a second program. Those other nodes then return their results directly to the manager.

Even further subdivisions are sometimes considered[8].

1. ^ Flynn, M., Some Computer Organizations and Their Effectiveness, IEEE Trans. Comput., Vol. C-21, pp. 948, 1972.
2. ^ Duncan, Ralph, "A Survey of Parallel Computer Architectures", IEEE Computer. February 1990, pp. 5-16.

This article was originally based on material from the Free On-line Dictionary of Computing, which is licensed under the GFDL.

• Michael J. Flynn

	Topics in Parallel Computing	v • d • e
General High-performance computing Parallelism Data parallelism • Task parallelism Theory Speedup • Amdahl's law • Flynn's Taxonomy • Cost efficiency • Gustafson's Law • Karp-Flatt Metric Elements Process • Thread • Fiber • Parallel Random Access Machine Coordination Multiprocessing • Multitasking • Memory coherency • Cache coherency • Barrier • Synchronization • Distributed computing • Grid computing Programming Programming model • Implicit parallelism • Explicit parallelism Hardware Computer cluster • Beowulf • Symmetric multiprocessing • Non-Uniform Memory Access • Cache only memory architecture • Asymmetric multiprocessing • Simultaneous multithreading • Shared memory • Distributed memory • Massively parallel processing • Superscalar processing • Vector processing • Supercomputer Software Distributed shared memory • Application checkpointing APIs Pthreads • OpenMP • Message Passing Interface (MPI) Problems Embarrassingly parallel • Grand Challenge