Abstract machine

Information

In the theory of computation, abstract machines are often used in thought experiments regarding computability or to analyze the complexity of algorithms (see computational complexity theory). A typical abstract machine consists of a definition in terms of input, output, and the set of allowable operations used to turn the former into the latter. The best-known example is the Turing machine.

Abstract data types can be specified in terms of their operational semantics on an abstract machine. For example, a stack can be specified in terms of operations on an abstract machine with an array of memory. Through the use of abstract machines it is possible to compute the amount of resources (time, memory, etc.) necessary to perform a particular operation without having to construct a physical system.^{[clarification needed]}

More complex definitions create abstract machines with full instruction sets, registers and models of memory. One popular model more similar to real modern machines is the RAM model, which allows random access to indexed memory locations. As the performance difference between different levels of cache memory grows, cache-sensitive models such as the external-memory model and cache-oblivious model are growing in importance.

An abstract machine can also refer to a microprocessor design which has yet to be (or is not intended to be) implemented as hardware. An abstract machine implemented as a software simulation, or for which an interpreter exists, is called a virtual machine.

See also

• Abstraction (computer science)
• Abstract interpretation
• Bulk synchronous parallel
• Discrete time
• Finite-state machine
• Flynn's taxonomy
• Formal models of computation
• Krivine machine
• Model of computation
• Parallel random-access machine, the de facto standard model.^[1]
• SECD machine
• State space
• Turing machine

References

Further reading

• Peter van Emde Boas, Machine Models and Simulations pp. 3–66, appearing in:

Jan van Leeuwen, ed. "Handbook of Theoretical Computer Science. Volume A: Algorithms and Complexity, The MIT PRESS/Elsevier, 1990. ISBN 0-444-88071-2 (volume A). QA 76.H279 1990.

• Stephan Diehl, Pieter Hartel and Peter Sestoft, Abstract Machines for Programming Language Implementation, Future Generation Computer Systems, Vol. 16(7), Elsevier, 2000.
• Werner Kluge (2006). Abstract Computing Machines: A Lambda Calculus Perspective. Springer. ISBN 978-3-540-27359-2.

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