# Register machine

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In mathematical logic and theoretical computer science a register machine is a generic class of abstract machines used in a manner similar to a Turing machine. All the models are Turing equivalent.

Mathematical logic is a subfield of mathematics exploring the applications of formal logic to mathematics. It bears close connections to metamathematics, the foundations of mathematics, and theoretical computer science. The unifying themes in mathematical logic include the study of the expressive power of formal systems and the deductive power of formal proof systems.

Theoretical computer science (TCS) is a subset of general computer science and mathematics that focuses on more mathematical topics of computing and includes the theory of computation.

An abstract machine, also called an abstract computer, is a theoretical model of a computer hardware or software system used in automata theory. Abstraction of computing processes is used in both the computer science and computer engineering disciplines and usually assumes a discrete time paradigm.

## Overview

The register machine gets its name from its use of one or more "registers". In contrast to the tape and head used by a Turing machine, the model uses multiple, uniquely addressed registers, each of which holds a single positive integer.

In computer architecture, a processor register is a quickly accessible location available to a computer's central processing unit (CPU). Registers usually consist of a small amount of fast storage, although some registers have specific hardware functions, and may be read-only or write-only. Registers are typically addressed by mechanisms other than main memory, but may in some cases be assigned a memory address e.g. DEC PDP-10, ICT 1900.

An integer is a number that can be written without a fractional component. For example, 21, 4, 0, and −2048 are integers, while 9.75, 5+1/2, and 2 are not.

There are at least four sub-classes found in literature, here listed from most primitive to the most like a computer:

A computer is a machine that can be instructed to carry out sequences of arithmetic or logical operations automatically via computer programming. Modern computers have the ability to follow generalized sets of operations, called programs. These programs enable computers to perform an extremely wide range of tasks. A "complete" computer including the hardware, the operating system, and peripheral equipment required and used for "full" operation can be referred to as a computer system. This term may as well be used for a group of computers that are connected and work together, in particular a computer network or computer cluster.

• Counter machine – the most primitive and reduced theoretical model of a computer hardware. Lacks indirect addressing. Instructions are in the finite state machine in the manner of the Harvard architecture.
• Pointer machine – a blend of counter machine and RAM models. Less common and more abstract than either model. Instructions are in the finite state machine in the manner of the Harvard architecture.
• Random-access machine (RAM) – a counter machine with indirect addressing and, usually, an augmented instruction set. Instructions are in the finite state machine in the manner of the Harvard architecture.
• Random-access stored-program machine model (RASP) – a RAM with instructions in its registers analogous to the Universal Turing machine; thus it is an example of the von Neumann architecture. But unlike a computer, the model is idealized with effectively infinite registers (and if used, effectively infinite special registers such as an accumulator). Unlike a computer or even RISC [ dubious ], the instruction set is much reduced in number.

A counter machine is an abstract machine used in a formal logic and theoretical computer science to model computation. It is the most primitive of the four types of register machines. A counter machine comprises a set of one or more unbounded registers, each of which can hold a single non-negative integer, and a list of arithmetic and control instructions for the machine to follow. The counter machine is typically used in the process of designing parallel algorithms in relation to the mutual exclusion principle. When used in this manner, the counter machine is used to model the discrete time-steps of a computational system in relation to memory accesses. By modeling computations in relation to the memory accesses for each respective computational step, parallel algorithms may be designed in such a matter to avoid interlocking, the simultaneous writing operation by two threads to the same memory address.

The Harvard architecture is a computer architecture with separate storage and signal pathways for instructions and data. It contrasts with the von Neumann architecture, where program instructions and data share the same memory and pathways.

In theoretical computer science a pointer machine is an "atomistic" abstract computational machine model akin to the random-access machine.

Any properly defined register machine model is Turing equivalent. Computational speed is very dependent on the model specifics.

In computability theory, a system of data-manipulation rules is said to be Turing complete or computationally universal if it can be used to simulate any Turing machine. This means that this system is able to recognize or decide other data-manipulation rule sets. Turing completeness is used as a way to express the power of such a data-manipulation rule set. Virtually all programming languages today are Turing complete. The concept is named after English mathematician and computer scientist Alan Turing.

In practical computer science, a similar concept known as a virtual machine is sometimes used to minimise dependencies on underlying machine architectures. Such machines are also used for teaching. The term "register machine" is sometimes used to refer to a virtual machine in textbooks. [1]

In computing, a virtual machine (VM) is an emulation of a computer system. Virtual machines are based on computer architectures and provide functionality of a physical computer. Their implementations may involve specialized hardware, software, or a combination.

## Formal definition

No standard terminology exists; each author is responsible for defining in prose the meanings of their mnemonics or symbols. Many authors use a "register-transfer"-like symbolism to explain the actions of their models, but again they are responsible for defining its syntax.

A register machine consists of:

1. An unbounded number of labeled, discrete, unbounded registers unbounded in extent (capacity): a finite (or infinite in some models) set of registers ${\displaystyle r_{0}\ldots r_{n}}$ each considered to be of infinite extent and each of which holds a single non-negative integer (0, 1, 2, ...). [2] The registers may do their own arithmetic, or there may be one or more special registers that do the arithmetic e.g. an "accumulator" and/or "address register". See also Random-access machine.
2. Tally counters or marks: [3] discrete, indistinguishable objects or marks of only one sort suitable for the model. In the most-reduced counter machine model, per each arithmetic operation only one object/mark is either added to or removed from its location/tape. In some counter machine models (e.g. Melzak (1961), Minsky (1961)) and most RAM and RASP models more than one object/mark can be added or removed in one operation with "addition" and usually "subtraction"; sometimes with "multiplication" and/or "division". Some models have control operations such as "copy" (variously: "move", "load", "store") that move "clumps" of objects/marks from register to register in one action.
3. A (very) limited set of instructions: the instructions tend to divide into two classes: arithmetic and control. The instructions are drawn from the two classes to form "instruction-sets", such that an instruction set must allow the model to be Turing equivalent (it must be able to compute any partial recursive function).
1. Arithmetic: arithmetic instructions may operate on all registers or on just a special register (e.g. accumulator). They are usually chosen from the following sets (but exceptions abound):
• Counter machine: { Increment (r), Decrement (r), Clear-to-zero (r) }
• Reduced RAM, RASP: { Increment (r), Decrement (r), Clear-to-zero (r), Load-immediate-constant k, Add (r1,r2), proper-Subtract (r1,r2), Increment accumulator, Decrement accumulator, Clear accumulator, Add to accumulator contents of register r, proper-Subtract from accumulator contents of register r, }
• Augmented RAM, RASP: All of the reduced instructions plus: { Multiply, Divide, various Boolean bit-wise (left-shift, bit test, etc.)}
2. Control:
• Counter machine models: optional { Copy (r1,r2) }
• RAM and RASP models: most have { Copy (r1,r2) }, or { Load Accumulator from r, Store accumulator into r, Load Accumulator with immediate constant }
• All models: at least one conditional "jump" (branch, goto) following test of a register e.g. { Jump-if-zero, Jump-if-not-zero (i.e. Jump-if-positive), Jump-if-equal, Jump-if-not equal }
• All models optional: { unconditional program jump (goto) }
• Counter machine: no indirect addressing, immediate operands possible in highly atomized models
• RAM and RASP: indirect addressing available, immediate operands typical
4. Input-output: optional in all models
4. State register: A special Instruction Register "IR", finite and separate from the registers above, stores the current instruction to be executed and its address in the TABLE of instructions; this register and its TABLE is located in the finite state machine.
• The IR is off-limits to all models. In the case of the RAM and RASP, for purposes of determining the "address" of a register, the model can select either (i) in the case of direct addressing—the address specified by the TABLE and temporarily located in the IR or (ii) in the case of indirect addressing—the contents of the register specified by the IR's instruction.
• The IR is not the "program counter" (PC) of the RASP (or conventional computer). The PC is just another register similar to an accumulator, but dedicated to holding the number of the RASP's current register-based instruction. Thus a RASP has two "instruction/program" registers—(i) the IR (finite state machine's Instruction Register), and (ii) a PC (Program Counter) for the program located in the registers. (As well as a register dedicated to "the PC", a RASP may dedicate another register to "the Program-Instruction Register" (going by any number of names such as "PIR, "IR", "PR", etc.)
5. List of labeled instructions, usually in sequential order: A finite list of instructions ${\displaystyle I_{1}\ldots I_{m}}$. In the case of the counter machine, random-access machine (RAM) and pointer machine the instruction store is in the "TABLE" of the finite state machine; thus these models are example of the Harvard architecture. In the case of the RASP the program store is in the registers; thus this is an example of the von Neumann architecture. See also Random-access machine and Random-access stored-program machine.
Usually, like computer programs, the instructions are listed in sequential order; unless a jump is successful the default sequence continues in numerical order. An exception to this is the abacus (Lambek (1961), Minsky (1961)) counter machine models—every instruction has at least one "next" instruction identifier "z", and the conditional branch has two.
• Observe also that the abacus model combines two instructions, JZ then DEC: e.g. { INC ( r, z ), JZDEC ( r, ztrue, zfalse ) }.
See McCarthy Formalism for more about the conditional expression "IF r=0 THEN ztrue ELSE zfalse" (cf McCarthy (1960)).

## Historical development of the register machine model

Two trends appeared in the early 1950s—the first to characterize the computer as a Turing machine, the second to define computer-like models—models with sequential instruction sequences and conditional jumps—with the power of a Turing machine, i.e. a so-called Turing equivalence. Need for this work was carried out in context of two "hard" problems: the unsolvable word problem posed by Emil Post—his problem of "tag"—and the very "hard" problem of Hilbert's problems—the 10th question around Diophantine equations. Researchers were questing for Turing-equivalent models that were less "logical" in nature and more "arithmetic" (cf Melzak (1961) p. 281, Shepherdson–Sturgis (1963) p. 218).

The first trend—toward characterizing computers—seems to have originated [4] with Hans Hermes (1954), Rózsa Péter (1958), and Heinz Kaphengst (1959), the second trend with Hao Wang (1954, 1957) and, as noted above, furthered along by Zdzislaw Alexander Melzak (1961), Joachim Lambek (1961), Marvin Minsky (1961, 1967), and John Shepherdson and Howard E. Sturgis (1963).

The last five names are listed explicitly in that order by Yuri Matiyasevich. He follows up with:

"Register machines [some authors use "register machine" synonymous with "counter-machine"] are particularly suitable for constructing Diophantine equations. Like Turing machines, they have very primitive instructions and, in addition, they deal with numbers" (Yuri Matiyasevich (1993), Hilbert's Tenth Problem, commentary to Chapter 5 of the book, at http://logic.pdmi.ras.ru/yumat/H10Pbook/commch_5htm. )

It appears that Lambek, Melzak, Minsky and Shepherdson and Sturgis independently anticipated the same idea at the same time. See Note On Precedence below.

The history begins with Wang's model.

### (1954, 1957) Wang's model: Post–Turing machine

Wang's work followed from Emil Post's (1936) paper and led Wang to his definition of his Wang B-machine—a two-symbol Post–Turing machine computation model with only four atomic instructions:

{ LEFT, RIGHT, PRINT, JUMP_if_marked_to_instruction_z }

To these four both Wang (1954, 1957) and then C.Y. Lee (1961) added another instruction from the Post set { ERASE }, and then a Post's unconditional jump { JUMP_to_ instruction_z } (or to make things easier, the conditional jump JUMP_IF_blank_to_instruction_z, or both. Lee named this a "W-machine" model:

{ LEFT, RIGHT, PRINT, ERASE, JUMP_if_marked, [maybe JUMP or JUMP_IF_blank] }

Wang expressed hope that his model would be "a rapprochement" (p. 63) between the theory of Turing machines and the practical world of the computer.

Wang's work was highly influential. We find him referenced by Minsky (1961) and (1967), Melzak (1961), Shepherdson and Sturgis (1963). Indeed, Shepherdson and Sturgis (1963) remark that:

"...we have tried to carry a step further the 'rapprochement' between the practical and theoretical aspects of computation suggested by Wang" (p. 218)

Martin Davis eventually evolved this model into the (2-symbol) Post–Turing machine.

Difficulties with the Wang/Post–Turing model:

Except there was a problem: the Wang model (the six instructions of the 7-instruction Post–Turing machine) was still a single-tape Turing-like device, however nice its sequential program instruction-flow might be. Both Melzak (1961) and Shepherdson and Sturgis (1963) observed this (in the context of certain proofs and investigations):

"...a Turing machine has a certain opacity... a Turing machine is slow in (hypothetical) operation and, usually, complicated. This makes it rather hard to design it, and even harder to investigate such matters as time or storage optimization or a comparison between efficiency of two algorithms. (Melzak (1961) p. 281)
"...although not difficult ... proofs are complicated and tedious to follow for two reasons: (1) A Turing machine has only head so that one is obliged to break down the computation into very small steps of operations on a single digit. (2) It has only one tape so that one has to go to some trouble to find the number one wishes to work on and keep it separate from other numbers" (Shepherdson and Sturgis (1963) p. 218).

Indeed, as examples at Turing machine examples, Post–Turing machine and partial function show, the work can be "complicated".

### Minsky, Melzak-Lambek and Shepherdson–Sturgis models "cut the tape" into many

So why not 'cut the tape' so each is infinitely long (to accommodate any size integer) but left-ended, and call these three tapes "Post–Turing (i.e. Wang-like) tapes"? The individual heads will move left (for decrement) and right (for increment). In one sense the heads indicate "the tops of the stack" of concatenated marks. Or in Minsky (1961) and Hopcroft and Ullman (1979, p. 171ff) the tape is always blank except for a mark at the left end—at no time does a head ever print or erase.

We just have to be careful to write our instructions so that a test-for-zero and jump occurs before we decrement otherwise our machine will "fall off the end" or "bump against the end"—we will have an instance of a partial function. Before a decrement our machine must always ask the question: "Is the tape/counter empty? If so then I can't decrement, otherwise I can."

For an example of (im-) proper subtraction see Partial function.

Minsky (1961) and Shepherdson–Sturgis (1963) prove that only a few tapes—as few as one—still allow the machine to be Turing equivalent IF the data on the tape is represented as a Gödel number (or some other uniquely encodable-decodable number); this number will evolve as the computation proceeds. In the one tape version with Gödel number encoding the counter machine must be able to (i) multiply the Gödel number by a constant (numbers "2" or "3"), and (ii) divide by a constant (numbers "2" or "3") and jump if the remainder is zero. Minsky (1967) shows that the need for this bizarre instruction set can be relaxed to { INC (r), JZDEC (r, z) } and the convenience instructions { CLR (r), J (r) } if two tapes are available. A simple Gödelization is still required, however. A similar result appears in Elgot–Robinson (1964) with respect to their RASP model.

### (1961) Melzak's model is different: clumps of pebbles go into and out of holes

Melzak's (1961) model is significantly different. He took his own model, flipped the tapes vertically, called them "holes in the ground" to be filled with "pebble counters". Unlike Minsky's "increment" and "decrement", Melzak allowed for proper subtraction of any count of pebbles and "adds" of any count of pebbles.

He defines indirect addressing for his model (p. 288) and provides two examples of its use (p. 89); his "proof" (p. 290-292) that his model is Turing equivalent is so sketchy that the reader cannot tell whether or not he intended the indirect addressing to be a requirement for the proof.

Legacy of Melzak's model is Lambek's simplification and the reappearance of his mnemonic conventions in Cook and Reckhow 1973.

### Lambek (1961) atomizes Melzak's model into the Minsky (1961) model: INC and DEC-with-test

Lambek (1961) took Melzak's ternary model and atomized it down to the two unary instructions—X+, X- if possible else jump—exactly the same two that Minsky (1961) had come up with.

However, like the Minsky (1961) model, the Lambek model does execute its instructions in a default-sequential manner—both X+ and X- carry the identifier of the next instruction, and X- also carries the jump-to instruction if the zero-test is successful.

### Elgot–Robinson (1964) and the problem of the RASP without indirect addressing

A RASP or random-access stored-program machine begins as a counter machine with its "program of instruction" placed in its "registers". Analogous to, but independent of, the finite state machine's "Instruction Register", at least one of the registers (nicknamed the "program counter" (PC)) and one or more "temporary" registers maintain a record of, and operate on, the current instruction's number. The finite state machine's TABLE of instructions is responsible for (i) fetching the current program instruction from the proper register, (ii) parsing the program instruction, (iii) fetching operands specified by the program instruction, and (iv) executing the program instruction.

Except there is a problem: If based on the counter machine chassis this computer-like, von Neumann machine will not be Turing equivalent. It cannot compute everything that is computable. Intrinsically the model is bounded by the size of its (very-) finite state machine's instructions. The counter machine based RASP can compute any primitive recursive function (e.g. multiplication) but not all mu recursive functions (e.g. the Ackermann function ).

Elgot–Robinson investigate the possibility of allowing their RASP model to "self modify" its program instructions. The idea was an old one, proposed by Burks-Goldstine-von Neumann (1946-7), and sometimes called "the computed goto." Melzak (1961) specifically mentions the "computed goto" by name but instead provides his model with indirect addressing.

Computed goto: A RASP program of instructions that modifies the "goto address" in a conditional- or unconditional-jump program instruction.

But this does not solve the problem (unless one resorts to Gödel numbers). What is necessary is a method to fetch the address of a program instruction that lies (far) "beyond/above" the upper bound of the finite state machine's instruction register and TABLE.

Example: A counter machine equipped with only four unbounded registers can e.g. multiply any two numbers ( m, n ) together to yield p—and thus be a primitive recursive function—no matter how large the numbers m and n; moreover, less than 20 instructions are required to do this! e.g. { 1: CLR ( p ), 2: JZ ( m, done ), 3 outer_loop: JZ ( n, done ), 4: CPY ( m, temp ), 5: inner_loop: JZ ( m, outer_loop ), 6: DEC ( m ), 7: INC ( p ), 8: J ( inner_loop ), 9: outer_loop: DEC ( n ), 10 J ( outer_loop ), HALT }
However, with only 4 registers, this machine has not nearly big enough to build a RASP that can execute the multiply algorithm as a program. No matter how big we build our finite state machine there will always be a program (including its parameters) which is larger. So by definition the bounded program machine that does not use unbounded encoding tricks such as Gödel numbers cannot be universal.

Minsky (1967) hints at the issue in his investigation of a counter machine (he calls them "program computer models") equipped with the instructions { CLR (r), INC (r), and RPT ("a" times the instructions m to n) }. He doesn't tell us how to fix the problem, but he does observe that:

"... the program computer has to have some way to keep track of how many RPT's remain to be done, and this might exhaust any particular amount of storage allowed in the finite part of the computer. RPT operations require infinite registers of their own, in general, and they must be treated differently from the other kinds of operations we have considered." (p. 214)

But Elgot and Robinson solve the problem: They augment their P0 RASP with an indexed set of instructions—a somewhat more complicated (but more flexible) form of indirect addressing. Their P'0 model addresses the registers by adding the contents of the "base" register (specified in the instruction) to the "index" specified explicitly in the instruction (or vice versa, swapping "base" and "index"). Thus the indexing P'0 instructions have one more parameter than the non-indexing P0 instructions:

Example: INC ( rbase, index ) ; effective address will be [rbase] + index, where the natural number "index" is derived from the finite-state machine instruction itself.

### Hartmanis (1971)

By 1971 Hartmanis has simplified the indexing to indirection for use in his RASP model.

Indirect addressing: A pointer-register supplies the finite state machine with the address of the target register required for the instruction. Said another way: The contents of the pointer-register is the address of the "target" register to be used by the instruction. If the pointer-register is unbounded, the RAM, and a suitable RASP built on its chassis, will be Turing equivalent. The target register can serve either as a source or destination register, as specified by the instruction.

Note that the finite state machine does not have to explicitly specify this target register's address. It just says to the rest of the machine: Get me the contents of the register pointed to by my pointer-register and then do xyz with it. It must specify explicitly by name, via its instruction, this pointer-register (e.g. "N", or "72" or "PC", etc.) but it doesn't have to know what number the pointer-register actually contains (perhaps 279,431).

### Cook and Reckhow (1973) describe the RAM

Cook and Reckhow (1973) cite Hartmanis (1971) and simplify his model to what they call a random-access machine (RAM—i.e. a machine with indirection and the Harvard architecture). In a sense we are back to Melzak (1961) but with a much simpler model than Melzak's.

## Precedence

Minsky was working at the MIT Lincoln Laboratory and published his work there; his paper was received for publishing in the Annals of Mathematics on August 15, 1960 but not published until November 1961. While receipt occurred a full year before the work of Melzak and Lambek was received and published (received, respectively, May and June 15, 1961 and published side-by-side September 1961). That (i) both were Canadians and published in the Canadian Mathematical Bulletin, (ii) neither would have had reference to Minsky's work because it was not yet published in a peer-reviewed journal, but (iii) Melzak references Wang, and Lambek references Melzak, leads one to hypothesize that their work occurred simultaneously and independently.

Almost exactly the same thing happened to Shepherdson and Sturgis. Their paper was received in December 1961—just a few months after Melzak and Lambek's work was received. Again, they had little (at most 1 month) or no benefit of reviewing the work of Minsky. They were careful to observe in footnotes that papers by Ershov, Kaphengst and Peter had "recently appeared" (p. 219). These were published much earlier but appeared in the German language in German journals so issues of accessibility present themselves.

The final paper of Shepherdson and Sturgis did not appear in a peer-reviewed journal until 1963. And as they fairly and honestly note in their Appendix A, the 'systems' of Kaphengst (1959), Ershov (1958), Peter (1958) are all so similar to what results were obtained later as to be indistinguishable to a set of the following:

produce 0 i.e. 0 --> n
increment a number i.e. n+1 --> n
"i.e. of performing the operations which generate the natural numbers" (p. 246)
copy a number i.e. n --> m
to "change the course of a computation", either comparing two numbers or decrementing until 0

Indeed, Shepherson and Sturgis conclude

"The various minimal systems are very similar"( p. 246)

By order of publishing date the work of Kaphengst (1959), Ershov (1958), Peter (1958) were first.

## Bibliography

Background texts: The following bibliography of source papers includes a number of texts to be used as background. The mathematics that led to the flurry of papers about abstract machines in the 1950s and 1960s can be found in van Heijenoort (1967)—an assemblage of original papers spanning the 50 years from Frege (1879) to Gödel (1931). Davis (ed.) The Undecidable (1965) carries the torch onward beginning with Gödel (1931) through Gödel's (1964) postscriptum (p. 71); the original papers of Alan Turing (1936-7) and Emil Post (1936) are included in The Undecidable. The mathematics of Church, Rosser and Kleene that appear as reprints of original papers in The Undecidable is carried further in Kleene (1952), a mandatory text for anyone pursuing a deeper understanding of the mathematics behind the machines. Both Kleene (1952) and Davis (1958) are referenced by a number of the papers.

For a good treatment of the counter machine see Minsky (1967) Chapter 11 "Models similar to Digital Computers"—he calls the counter machine a "program computer". A recent overview is found at van Emde Boas (1990). A recent treatment of the Minsky (1961)/Lambek (1961) model can be found Boolos-Burgess-Jeffrey (2002); they reincarnate Lambek's "abacus model" to demonstrate equivalence of Turing machines and partial recursive functions, and they provide a graduate-level introduction to both abstract machine models (counter- and Turing-) and the mathematics of recursion theory. Beginning with the first edition Boolos-Burgess (1970) this model appeared with virtually the same treatment.

The papers: The papers begin with Wang (1957) and his dramatic simplification of the Turing machine. Turing (1936), Kleene (1952), Davis (1958) and in particular Post (1936) are cited in Wang (1957); in turn, Wang is referenced by Melzak (1961), Minsky (1961) and Shepherdson–Sturgis (1961-3) as they independently reduce the Turing tapes to "counters". Melzak (1961) provides his pebble-in-holes counter machine model with indirection but doesn't carry the treatment further. The work of Elgot–Robinson (1964) define the RASP—the computer-like random-access stored-program machines—and appear to be the first to investigate the failure of the bounded counter machine to calculate the mu-recursive functions. This failure—except with the draconian use of Gödel numbers in the manner of Minsky (1961))—leads to their definition of "indexed" instructions (i.e. indirect addressing) for their RASP model. Elgot–Robinson (1964) and more so Hartmanis (1971) investigate RASPs with self-modifying programs. Hartmanis (1971) specifies an instruction set with indirection, citing lecture notes of Cook (1970). For use in investigations of computational complexity Cook and his graduate student Reckhow (1973) provide the definition of a RAM (their model and mnemonic convention are similar to Melzak's, but offer him no reference in the paper). The pointer machines are an offshoot of Knuth (1968, 1973) and independently Schönhage (1980).

For the most part the papers contain mathematics beyond the undergraduate level—in particular the primitive recursive functions and mu recursive functions presented elegantly in Kleene (1952) and less in depth, but useful nonetheless, in Boolos-Burgess-Jeffrey (2002).

All texts and papers excepting the four starred have been witnessed. These four are written in German and appear as references in Shepherdson–Sturgis (1963) and Elgot–Robinson (1964); Shepherdson–Sturgis (1963) offer a brief discussion of their results in Shepherdson–Sturgis' Appendix A. The terminology of at least one paper (Kaphengst (1959) seems to hark back to the Burke-Goldstine-von Neumann (1946-7) analysis of computer architecture.

AuthorYearReferenceTuring machineCounter machineRAMRASPPointer machineIndirect addressingSelf-modifying program
Goldstine & von Neumann1947XX
Kleene1952X
*Hermes1954, 5?
Wang1957XXhintshints
*Peter1958?
Davis1958XX
*Ershov1959?
*Kaphengst1959?X
Melzak1961XXhints
Lambek1961X
Minsky1961X
Shepherdson & Sturgis1963Xhints
Elgot & Robinson1964XXX
Davis- Undecidable1965XX
van Heijenoort1967X
Minsky1967Xhintshints
Knuth1968, 73XXXX
Hartmanis1971XX
Cook & Reckhow1973XXXX
Schonhage1980XXX
van Emde Boas1990XXXXXX
Boolos & Burgess; Boolos, Burgess & Jeffrey1970–2002XXX

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In computer science, random-access machine (RAM) is an abstract machine in the general class of register machines. The RAM is very similar to the counter machine but with the added capability of 'indirect addressing' of its registers. Like the counter machine the RAM has its instructions in the finite-state portion of the machine.

Addressing modes are an aspect of the instruction set architecture in most central processing unit (CPU) designs. The various addressing modes that are defined in a given instruction set architecture define how machine language instructions in that architecture identify the operand(s) of each instruction. An addressing mode specifies how to calculate the effective memory address of an operand by using information held in registers and/or constants contained within a machine instruction or elsewhere.

A Post–Turing machine is a "program formulation" of an especially simple type of Turing machine, comprising a variant of Emil Post's Turing-equivalent model of computation described below. A Post–Turing machine uses a binary alphabet, an infinite sequence of binary storage locations, and a primitive programming language with instructions for bi-directional movement among the storage locations and alteration of their contents one at a time. The names "Post–Turing program" and "Post–Turing machine" were used by Martin Davis in 1973–1974. Later in 1980, Davis used the name "Turing–Post program".

NAR 1 or just NAR was a theoretical model of a computer created by Faculty of Mathematics of University of Belgrade professor Nedeljko Parezanović. It was used for Assembly language and Computer architecture courses.

NAR 2 is a theoretical model of a 32-bit word computer created by Faculty of Mathematics of University of Belgrade professor Nedeljko Parezanović as an enhancement to its predecessor, NAR 1. It was used for Assembly language and Computer architecture courses. The word "nar" means Pomegranate in Serbian. Many NAR 2 simulators have been created — for instance, one was named "Šljiva" as that fruit grows in Serbia, while "nar" does not.

A Turing machine is a hypothetical computing device, first conceived by Alan Turing in 1936. Turing machines manipulate symbols on a potentially infinite strip of tape according to a finite table of rules, and they provide the theoretical underpinnings for the notion of a computer algorithm.

As presented by Hao Wang, his basic machine B is an extremely simple computational model equivalent to the Turing machine. It is "the first formulation of a Turing-machine theory in terms of computer-like models". With only 4 sequential instructions it is very similar to, but even simpler than, the 7 sequential instructions of the Post–Turing machine. In the same paper, Wang introduced a variety of equivalent machines, including what he called the W-machine, which is the B-machine with an "erase" instruction added to the instruction set.

In theoretical computer science the random-access stored-program (RASP) machine model is an abstract machine used for the purposes of algorithm development and algorithm complexity theory.

## References

Notes

1. Harold Abelson and Gerald Jay Sussman with Julie Sussman, Structure and Interpretation of Computer Programs, MIT Press, Cambridge, Massachusetts, 2nd Ed, 1996
2. ". . . a denumerable sequence of registers numbered 1, 2, 3, ..., each of which can sto3e any natural number 0, 1, 2, .... Each particular program, however, involves only a finite number of these registers, the others remaining empty (i.e. containing 0) throughout the computation." Shepherdson and Sturgis 1961:219. Lambek 1961:295 proposed: "a countably infinite set of locations (holes, wires, etc).
3. For example, Lambek 1961:295 proposed the use of pebbles, beads, etc.
4. See the "Note" in Shepherdson and Sturgis 1963:219. In their Appendix A the authors follow up with a listing and discussions of Kaphengst's, Ershov's and Péter's instruction sets (cf p. 245ff).

Sources

• George Boolos, John P. Burgess, Richard Jeffrey (2002), Computability and Logic: Fourth Edition, Cambridge University Press, Cambridge, England. The original Boolos-Jeffrey text has been extensively revised by Burgess: more advanced than an introductory textbook. "Abacus machine" model is extensively developed in Chapter 5 Abacus Computability; it is one of three models extensively treated and compared—the Turing machine (still in Boolos' original 4-tuple form) and recursion the other two.
• Arthur Burks, Herman Goldstine, John von Neumann (1946), "Preliminary discussion of the logical design of an electronic computing instrument", reprinted pp. 92ff in Gordon Bell and Allen Newell (1971), Computer Structures: Readings and Examples, McGraw-Hill Book Company, New York. ISBN   0-07-004357-4 .
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• Martin Davis (1958), Computability & Unsolvability, McGraw-Hill Book Company, Inc. New York.
• Calvin Elgot and Abraham Robinson (1964), "Random-Access Stored-Program Machines, an Approach to Programming Languages", Journal of the Association for Computing Machinery, Vol. 11, No. 4 (October, 1964), pp. 365–399.
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• John Hopcroft, Jeffrey Ullman (1979). Introduction to Automata Theory, Languages and Computation, 1st ed., Reading Mass: Addison-Wesley. ISBN   0-201-02988-X. A difficult book centered around the issues of machine-interpretation of "languages", NP-Completeness, etc.
• Stephen Kleene (1952), Introduction to Metamathematics, North-Holland Publishing Company, Amsterdam, Netherlands. ISBN   0-7204-2103-9.
• Donald Knuth (1968), The Art of Computer Programming, Second Edition 1973, Addison-Wesley, Reading, Massachusetts. Cf pages 462-463 where he defines "a new kind of abstract machine or 'automaton' which deals with linked structures."
• Joachim Lambek (1961, received 15 June 1961), "How to Program an Infinite Abacus", Mathematical Bulletin, vol. 4, no. 3. September 1961 pages 295-302. In his Appendix II, Lambek proposes a "formal definition of 'program'. He references Melzak (1961) and Kleene (1952) Introduction to Metamathematics.
• Z. A. Melzak (1961, received 15 May 1961), "An informal Arithmetical Approach to Computability and Computation", Canadian Mathematical Bulletin, vol. 4, no. 3. September 1961 pages 279-293. Melzak offers no references but acknowledges "the benefit of conversations with Drs. R. Hamming, D. McIlroy and V. Vyssots of the Bell telephone Laborators and with Dr. H. Wang of Oxford University."
• Minsky, Marvin (1961). "Recursive Unsolvability of Post's Problem of 'Tag' and Other Topics in Theory of Turing Machines". Annals of Mathematics. 74 (3): 437–455. doi:10.2307/1970290. JSTOR   1970290.
• Minsky, Marvin (1967). (1st ed.). Englewood Cliffs, NJ: Prentice-Hall, Inc. In particular see chapter 11: Models Similar to Digital Computers and chapter 14: Very Simple Bases for Computability. In the former chapter he defines "Program machines" and in the later chapter he discusses "Universal Program machines with Two Registers" and "...with one register", etc.
• John C. Shepherdson and H. E. Sturgis (1961) received December 1961 "Computability of Recursive Functions", Journal of the Association of Computing Machinery (JACM) 10:217-255, 1963. An extremely valuable reference paper. In their Appendix A the authors cite 4 others with reference to "Minimality of Instructions Used in 4.1: Comparison with Similar Systems".
• Kaphengst, Heinz, "Eine Abstrakte programmgesteuerte Rechenmaschine", Zeitschrift fur mathematische Logik und Grundlagen der Mathematik 5 (1959), 366-379.
• Ershov, A. P. "On operator algorithms", (Russian) Dok. Akad. Nauk 122 (1958), 967-970. English translation, Automat. Express 1 (1959), 20-23.
• Péter, Rózsa "Graphschemata und rekursive Funktionen", Dialectica 12 (1958), 373.
• Hermes, Hans "Die Universalität programmgesteuerter Rechenmaschinen". Math.-Phys. Semesterberichte (Göttingen) 4 (1954), 42-53.
• Arnold Schönhage (1980), Storage Modification Machines, Society for Industrial and Applied Mathematics, SIAM J. Comput. Vol. 9, No. 3, August 1980. Wherein Schōnhage shows the equivalence of his SMM with the "successor RAM" (Random Access Machine), etc. resp. Storage Modification Machines, in Theoretical Computer Science (1979), pp. 36–37
• Peter van Emde Boas, "Machine Models and Simulations" pp. 3–66, 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. van Emde Boas' treatment of SMMs appears on pp. 32–35. This treatment clarifies Schōnhage 1980—it closely follows but expands slightly the Schōnhage treatment. Both references may be needed for effective understanding.
• Hao Wang (1957), "A Variant to Turing's Theory of Computing Machines", JACM (Journal of the Association for Computing Machinery) 4; 63-92. Presented at the meeting of the Association, June 23–25, 1954.