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Multiprocessing is the use of two or more central processing units (CPUs) within a single computer system.[1][2] The term also refers to the ability of a system to support more than one processor and/or the ability to allocate tasks between them.[3] There are many variations on this basic theme, and the definition of multiprocessing can vary with context, mostly as a function of how CPUs are defined (multiple cores on one die, multiple dies in one package, multiple packages in one system unit, etc.).

According to some on-line dictionaries, a multiprocessor is a computer system having two or more processing units (multiple processors) each sharing main memory and peripherals, in order to simultaneously process programs.[4][5] A 2009 textbook defined multiprocessor system similarly, but noting that the processors may share "some or all of the system’s memory and I/O facilities"; it also gave tightly coupled system as a synonymous term.[6]

At the operating system level, multiprocessing is sometimes used to refer to the execution of multiple concurrent processes in a system as opposed to a single process at any one instant.[7][8] When used with this definition, multiprocessing is sometimes contrasted with multitasking, which may use just a single processor but switch it in time slices between tasks (i.e. a time-sharing system). Multiprocessing however means true parallel execution of multiple processes using more than one processor.[8] Multiprocessing doesn't necessarily mean that a single process or task uses more than one processor simultaneously; the term parallel processing is generally used to denote that scenario.[7] Other authors prefer to refer to the operating system techniques as multiprogramming and reserve the term multiprocessing for the hardware aspect of having more than one processor.[9][2] The remainder of this article discusses multiprocessing only in this hardware sense.

In Flynn's taxonomy, multiprocessors as defined above are MIMD machines.[10][11] As they are normally construed to be tightly coupled (share memory), multiprocessors are not the entire class of MIMD machines, which also contains message passing multicomputer systems.[10]


  • Pre-history 1
  • Key topics 2
    • Processor symmetry 2.1
    • Instruction and data streams 2.2
    • Processor coupling 2.3
      • Tightly coupled multiprocessor system 2.3.1
      • Loosely coupled multiprocessor system 2.3.2
    • Multiprocessor Communication Architecture 2.4
      • Message passing 2.4.1
      • Shared memory 2.4.2
  • Flynn's taxonomy 3
    • SISD multiprocessing 3.1
    • SIMD multiprocessing 3.2
    • MISD multiprocessing 3.3
    • MIMD multiprocessing 3.4
  • See also 4
  • References 5


According to a 1985 article in Byte, possibly the first expression of the idea of multiprocessing is found in the 1842 words of Federico Luigi Menabrea, which said about Charles Babbage's analytical engine: "The machine can be brought into play so as to give several results at the same time, which will greatly abridge the whole amount of processes."[12]

Key topics

Processor symmetry

In a multiprocessing system, all CPUs may be equal, or some may be reserved for special purposes. A combination of hardware and operating system software design considerations determine the symmetry (or lack thereof) in a given system. For example, hardware or software considerations may require that only one particular CPU respond to all hardware interrupts, whereas all other work in the system may be distributed equally among CPUs; or execution of kernel-mode code may be restricted to only one particular CPU, whereas user-mode code may be executed in any combination of processors. Multiprocessing systems are often easier to design if such restrictions are imposed, but they tend to be less efficient than systems in which all CPUs are utilized.

Systems that treat all CPUs equally are called symmetric multiprocessing (SMP) systems. In systems where all CPUs are not equal, system resources may be divided in a number of ways, including asymmetric multiprocessing (ASMP), non-uniform memory access (NUMA) multiprocessing, and clustered multiprocessing.

Instruction and data streams

In multiprocessing, the processors can be used to execute a single sequence of instructions in multiple contexts (single-instruction, multiple-data or SIMD, often used in vector processing), multiple sequences of instructions in a single context (multiple-instruction, single-data or MISD, used for redundancy in fail-safe systems and sometimes applied to describe pipelined processors or hyper-threading), or multiple sequences of instructions in multiple contexts (multiple-instruction, multiple-data or MIMD).

Processor coupling

Tightly coupled multiprocessor system

Tightly coupled multiprocessor systems contain multiple CPUs that are connected at the bus level. These CPUs may have access to a central shared memory (SMP or UMA), or may participate in a memory hierarchy with both local and shared memory (NUMA). The IBM p690 Regatta is an example of a high end SMP system. Intel Xeon processors dominated the multiprocessor market for business PCs and were the only major x86 option until the release of AMD's Opteron range of processors in 2004. Both ranges of processors had their own onboard cache but provided access to shared memory; the Xeon processors via a common pipe and the Opteron processors via independent pathways to the system RAM.

Chip multiprocessors, also known as multi-core computing, involves more than one processor placed on a single chip and can be thought of the most extreme form of tightly-coupled multiprocessing. Mainframe systems with multiple processors are often tightly-coupled.

Loosely coupled multiprocessor system

Loosely coupled multiprocessor systems (often referred to as clusters) are based on multiple standalone single or dual processor commodity computers interconnected via a high speed communication system (Gigabit Ethernet is common). A Linux Beowulf cluster is an example of a loosely-coupled system.

Tightly-coupled systems perform better and are physically smaller than loosely-coupled systems, but have historically required greater initial investments and may depreciate rapidly; nodes in a loosely coupled system are usually inexpensive commodity computers and can be recycled as independent machines upon retirement from the cluster.

Power consumption is also a consideration. Tightly coupled systems tend to be much more energy efficient than clusters. This is because considerable economy can be realized by designing components to work together from the beginning in tightly coupled systems, whereas loosely coupled systems use components that were not necessarily intended specifically for use in such systems.

Loosely-coupled systems have the ability to run different operating systems or OS versions on different systems.

Multiprocessor Communication Architecture

Message passing

Separate address space for each processor.

processors communicate via message passing.

processors have provide memories.

focus attention on costly non-local operations.

Shared memory

Processors communicate with shared address space

Processors communicate by memory read/write

Easy on small-scale machines

Lower latency

SMP or NUMA architecture

Flynn's taxonomy

Flynn's taxonomy (multiprogramming context)
  Single instruction Multiple instruction Single program Multiple program
Single data SISD MISD

SISD multiprocessing

In a single-instruction stream, single-data stream computer one processor sequentially processes instructions, each instruction processes one data item. One example is the "von Neumann" architecture with RISC.

SIMD multiprocessing

In a single-instruction stream, multiple data stream computer one processor handles a stream of instructions, each one of which can perform calculations in parallel on multiple data locations.

SIMD multiprocessing is well suited to parallel or vector processing, in which a very large set of data can be divided into parts that are individually subjected to identical but independent operations. A single instruction stream directs the operation of multiple processing units to perform the same manipulations simultaneously on potentially large amounts of data.

For certain types of computing applications, this type of architecture can produce enormous increases in performance, in terms of the elapsed time required to complete a given task. However, a drawback to this architecture is that a large part of the system falls idle when programs or system tasks are executed that cannot be divided into units that can be processed in parallel.

Additionally, programs must be carefully and specially written to take maximum advantage of the architecture, and often special optimizing compilers designed to produce code specifically for this environment must be used. Some compilers in this category provide special constructs or extensions to allow programmers to directly specify operations to be performed in parallel (e.g., DO FOR ALL statements in the version of FORTRAN used on the ILLIAC IV, which was a SIMD multiprocessing supercomputer).

SIMD multiprocessing finds wide use in certain domains such as computer simulation, but is of little use in general-purpose desktop and business computing environments.

MISD multiprocessing

MISD multiprocessing offers mainly the advantage of redundancy, since multiple processing units perform the same tasks on the same data, reducing the chances of incorrect results if one of the units fails. MISD architectures may involve comparisons between processing units to detect failures. Apart from the redundant and fail-safe character of this type of multiprocessing, it has few advantages, and it is very expensive. It does not improve performance. It can be implemented in a way that is transparent to software. It is used in array processors and is implemented in fault tolerant machines.

Another example of MISD is pipelined image processing where every image pixel is piped through several hardware units performing several steps of image transformation.

MIMD multiprocessing

MIMD multiprocessing architecture is suitable for a wide variety of tasks in which completely independent and parallel execution of instructions touching different sets of data can be put to productive use. For this reason, and because it is easy to implement, MIMD predominates in multiprocessing.

Processing is divided into multiple threads, each with its own hardware processor state, within a single software-defined process or within multiple processes. Insofar as a system has multiple threads awaiting dispatch (either system or user threads), this architecture makes good use of hardware resources.

MIMD does raise issues of deadlock and resource contention, however, since threads may collide in their access to resources in an unpredictable way that is difficult to manage efficiently. MIMD requires special coding in the operating system of a computer but does not require application changes unless the programs themselves use multiple threads (MIMD is transparent to single-threaded programs under most operating systems, if the programs do not voluntarily relinquish control to the OS). Both system and user software may need to use software constructs such as semaphores (also called locks or gates) to prevent one thread from interfering with another if they should happen to cross paths in referencing the same data. This gating or locking process increases code complexity, lowers performance, and greatly increases the amount of testing required, although not usually enough to negate the advantages of multiprocessing.

Similar conflicts can arise at the hardware level between processors (cache contention and corruption, for example), and must usually be resolved in hardware, or with a combination of software and hardware (e.g., cache-clear instructions).

See also


  1. ^ Raj Rajagopal (1999). Introduction to Microsoft Windows NT Cluster Server: Programming and Administration. CRC Press. p. 4.  
  2. ^ a b Mike Ebbers; John Kettner; Wayne O'Brien; Bill Ogden, IBM Redbooks (2012). Introduction to the New Mainframe: z/OS Basics. IBM Redbooks. p. 96.  
  3. ^ Chip multiprocessing
  4. ^
  5. ^
  6. ^ Irv Englander (2009). The architecture of Computer Hardware and Systems Software. An Information Technology Approach. (4th ed.). Wiley. p. 265. 
  7. ^ a b Deborah Morley; Charles Parker (13 February 2012). Understanding Computers: Today and Tomorrow, Comprehensive. Cengage Learning. p. 183.  
  8. ^ a b Shibu K. V. Introduction to Embedded Systems. Tata McGraw-Hill Education. p. 402.  
  9. ^ Ashok Arora (2006). Foundations of Computer Science. Laxmi Publications. p. 149.  
  10. ^ a b Ran Giladi (2008). Network Processors: Architecture, Programming, and Implementation. Morgan Kaufmann. p. 293.  
  11. ^ Sajjan G. Shiva (20 September 2005). Advanced Computer Architectures. CRC Press. p. 221.  
  12. ^ Multiprocessing, BYTE magazine Volume 10, Number 05 (May 1985), p. 169
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