 Register machine

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.
Overview
The register machine gets its name from its one or more "registers"—in place of a Turing machine's tape and head (or tapes and heads) the model uses multiple, uniquelyaddressed registers, each of which holds a single positive integer.
There are at least 4 subclasses found in the literature, here listed from most primitive to the most like a computer:
 counter machine – the most primitive and reduced model. 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 effectivelyinfinite registers (and if used, effectivelyinfinite special registers such as an accumulator). Unlike a computer or even a RISC, the instruction set is much reduced in the number of instructions.
Any properlydefined register machine model is Turing equivalent. Computational speed is very dependent on the model specifics.
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]}
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 "registertransfer"like symbolism to explain the actions of their models, but again they are responsible for defining its syntax.
A register machine consists of:
 An unbounded number of labeled, discrete, unbounded registers unbounded in extent (capacity): a finite (or infinite in some models) set of registers each considered to be of infinite extent and each of which holds a single nonnegative 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.
 Tally counters or marks^{[3]}: discrete, indistinguishable objects or marks of only one sort suitable for the model. In the mostreduced 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.
 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 "instructionsets", such that an instruction set must allow the model to be Turing equivalent (it must be able to compute any partial recursive function).
 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), Cleartozero (r) }
 Reduced RAM, RASP: { Increment (r), Decrement (r), Cleartozero (r), Loadimmediateconstant k, Add (r_{1},r_{2}), properSubtract (r_{1},r_{2}), Increment accumulator, Decrement accumulator, Clear accumulator, Add to accumulator contents of register r, properSubtract from accumulator contents of register r, }
 Augmented RAM, RASP: All of the reduced instructions plus: { Multiply, Divide, various Boolean bitwise (leftshift, bit test, etc.)}
 Control:
 Counter machine models: optional { Copy (r_{1},r_{2}) }
 RAM and RASP models: most have { Copy (r_{1},r_{2}) }, 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. { Jumpifzero, Jumpifnotzero (i.e. Jumpifpositive), Jumpifequal, Jumpifnot equal }
 All models optional: { unconditional program jump (goto) }
 Registeraddressing method:
 Counter machine: no indirect addressing, immediate operands possible in highly atomized models
 RAM and RASP: indirect addressing available, immediate operands typical
 Inputoutput: optional in all models
 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):
 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 offlimits 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 registerbased 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 ProgramInstruction Register" (going by any number of names such as "PIR, "IR", "PR", etc.)
 List of labeled instructions, usually in sequential order: A finite list of instructions . 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, z_{true}, z_{false} ) }.
See McCarthy Formalism for more about the conditional expression "IF r=0 THEN z_{true} ELSE z_{false}" (cf McCarthy (1960)).
 Observe also that the abacus model combines two instructions, JZ then DEC: e.g. { INC ( r, z ), JZDEC ( r, z_{true}, z_{false} ) }.
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 computerlike models—models with sequential instruction sequences and conditional jumps—with the power of a Turing machine, i.e. a socalled 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 Turingequivalent models that were less "logical" in nature and more "arithmetic" (cf Melzak (1961) p. 281, ShepherdsonSturgis (1963) p. 218).
The first trend—toward characterizing computers—seems to have originated with Hans Hermes (1954) 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 "countermachine"] 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: PostTuring machine
Wang's work followed from Emil Post's (1936) paper and led Wang to his definition of his Wang Bmachine—a twosymbol PostTuring 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 other 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 "Wmachine" 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 (2symbol) PostTuring machine.
Difficulties with the Wang/PostTuring model:
Except there was a problem: the Wang model (the six instructions of the 7instruction PostTuring machine) was still a singletape Turinglike device, however nice its sequential program instructionflow 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 under one wishing to work on and keep it separate from other numbers" (Shepherdson and Sturgis (1963) p. 218).
Indeed as examples at Turing machine examples, PostTuring machine and partial function show, the work can be "complicated".
Minsky, MelzakLambek and ShepherdsonSturgis models "cut the tape" into many
So why not 'cut the tape' so each is infinitely long (to accommodate any size integer) but leftended, and call these three tapes "PostTuring (ie. Wanglike) 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 testforzero 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 example of the addition algorithm written for a counter machine see Algorithm examples, and for an example of (im) proper subtraction see Partial function.
Minsky (1961) and ShepherdsonSturgis (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 encodabledecodable 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 ElgotRobinson (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. 290292) 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 DECwithtest
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 defaultsequential manner—both X+ and X carry the identifier of the next instruction, and X also carries the jumpto instruction if the zerotest is successful.
ElgotRobinson (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, (ii) 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 computerlike, 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 ).
ElgotRobinson investigate the possibility of allowing their RASP model to "self modify" its program instructions. The idea was an old one, proposed by BurksGoldstinevon Neumann (19467), 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 unconditionaljump 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 not 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 P_{0} 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 nonindexing P_{0} instructions:
 Example: INC ( r_{base}, index ) ; effective address will be [r_{base}] + index, where the natural number "index" is derived from the finitestate machine instruction itself.
Hartmanis (1971)
By 1971 Hartmanis has simplified the indexing to indirection for use in his RASP model.
Indirect addressing: A pointerregister supplies the finite state machine with the address of the target register required for the instruction. Said another way: The contents of the pointerregister is the address of the "target" register to be used by the instruction. If the pointerregister 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 pointerregister and then do xyz with it. It must specify explicitly by name, via its instruction, this pointerregister (e.g. "N", or "72" or "PC", etc.) but it doesn't have to know what number the pointerregister 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 M.I.T. Lincoln Labs 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 sidebyside 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 peerreviewed 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 peerreviewed 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. Does context matter? An answer would require close examination of the papers. Conclusions and opinions about this will be left to the reader.
See also

 Counter machine:Reference model
 Counter machine models
 Wang Bmachine
 PostTuring machine  description plus examples
 Algorithm
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 (19367) 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 BoolosBurgessJeffrey (2002); they reincarnate Lambek's "abacus model" to demonstrate equivalence of Turing machines and partial recursive functions, and they provide a graduatelevel introduction to both abstract machine models (counter and Turing) and the mathematics of recursion theory. Beginning with the first edition BoolosBurgess (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 ShepherdsonSturgis (19613) as they independently reduce the Turing tapes to "counters". Melzak (1961) provides his pebbleinholes counter machine model with indirection but doesn't carry the treatment further. The work of ElgotRobinson (1964) define the RASP—the computerlike random access stored program machines—and appear to be the first to investigate the failure of the bounded counter machine to calculate the murecursive 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. ElgotRobinson (1964) and more so Hartmanis (1971) investigate RASPs with selfmodifying 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 BoolosBurgessJeffrey (2002).
All texts and papers excepting the four starred have been witnessed. These four are written in German and appear as references in ShepherdsonSturgis (1963) and ElgotRobinson (1964); ShepherdsonSturgis (1963) offer a brief discussion of their results in ShepherdsonSturgis' Appendix A. The terminology of at least one paper (Kaphengst (1959) seems to hark back to the BurkeGoldstinevon Neumann (19467) analysis of computer architecture.
Author Year Reference Turing machine Counter machine RAM RASP Pointer machine Indirect addressing Self modifying program Goldstine & von Neumann 1947 X X Kleene 1952 X *Hermes 1954, 5 ? Wang 1957 X X hints hints *Peter 1958 ? Davis 1958 X X *Ershov 1959 ? *Kaphengst 1959 ? X Melzak 1961 X X hints Lambek 1961 X Minsky 1961 X Shepherdson & Sturgis 1963 X hints Elgot & Robinson 1964 X X X Davis Undecidable 1965 X X van Heijenoort 1967 X Minsky 1967 X hints hints Knuth 1968, 73 X X X X Hartmanis 1971 X X Cook & Reckhow 1973 X X X X Schonhage 1980 X X X van Emde Boas 1990 X X X X X X Boolos & Burgess; Boolos, Burgess & Jeffrey 1970–2002 X X X References
 ^ Harold Abelson and Gerald Jay Sussman with Julie Sussman, Structure and Interpretation of Computer Programs, MIT Press, Cambridge, Massachusetts, 2nd Ed, 1996
 ^ ". . . 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).
 ^ For example, Lambek 1961:295 proposed the use of pebbles, beads, etc.
 George Boolos, John P. Burgess, Richard Jeffrey (2002), Computability and Logic: Fourth Edition, Cambridge University Press, Cambridge, England. The original BoolosJeffrey 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 4tuple 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, mcGrawHill Book Company, New York. ISBN 0070043574 .
 Stephen A. Cook and Robert A. Reckhow (1972), Timebounded random access machines, Journal of Computer Systems Science 7 (1973), 354375.
 Martin Davis (1958), Computability & Unsolvability, McGrawHill Book Company, Inc. New York.
 Calvin Elgot and Abraham Robinson (1964), RandomAccess StoredProgram Machines, an Approach to Programming Languages, Journal of the Association for Computing Machinery, Vol. 11, No. 4 (October, 1964), pp. 365–399.
 J. Hartmanis (1971), "Computational Complexity of Random Access Stored Program Machines," Mathematical Systems Theory 5, 3 (1971) pp. 232–245.
 John Hopcroft, Jeffrey Ullman (1979). Introduction to Automata Theory, Languages and Computation, 1st ed., Reading Mass: AddisonWesley. ISBN 020102988X. A difficult book centered around the issues of machineinterpretation of "languages", NPCompleteness, etc.
 Stephen Kleene (1952), Introduction to Metamathematics, NorthHolland Publishing Company, Amsterdam, Netherlands. ISBN 0720421039.
 Donald Knuth (1968), The Art of Computer Programming, Second Edition 1973, AddisonWesley, Reading, Massachusetts. Cf pages 462463 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 295302. 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 279293. 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."
 Marvin Minsky (1961, received August 15, 1960). "Recursive Unsolvability of Post's Problem of 'Tag' and Other Topics in Theory of Turing Machines". Annals of Math (The Annals of Mathematics, Vol. 74, No. 3) 74 (3): 437–455. doi:10.2307/1970290. JSTOR 1970290.
 Marvin Minsky (1967). Computation: Finite and Infinite Machines (1st ed.). Englewood Cliffs, N. J.: PrenticeHall, 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) 10:217255, 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), 366379.
 Ershov, A. P. On operator algorithms, (Russian) Dok. Akad. Nauk 122 (1958), 967970. English translation, Automat. Express 1 (1959), 2023.
 Péter, Rózsa Graphschemata und rekursive Funktionen, Dialectica 12 (1958), 373.
 Hermes, Hans Die Universalität programmgesteuerter Rechenmaschinen. Math.Phys. Semesterberichte (Göttingen) 4 (1954), 4253.
 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 0444880712 (volume A). QA 76.H279 1990. van Emde Boas' treatment of SMMs appears on pp. 3235. 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; 6392. Presented at the meeting of the Association, June 23–25, 1954.
External links
Categories: Models of computation
 Register machines
Wikimedia Foundation. 2010.
Look at other dictionaries:
unlimited register machine — noun A particular type of theoretical computer, with infinitely many memory cells called , and formal rules to determine the machines behavior based on the contents thereof. Syn: URM … Wiktionary
Machine a registres illimites — Machine à registres illimités En informatique, une machine à registres illimités ou URM (de l anglais : Unlimited Register Machine) est un modèle abstrait du fonctionnement des appareils mécaniques de calcul, tout comme les machines de… … Wikipédia en Français
Machine À Registres Illimités — En informatique, une machine à registres illimités ou URM (de l anglais : Unlimited Register Machine) est un modèle abstrait du fonctionnement des appareils mécaniques de calcul, tout comme les machines de Turing et le lambda calcul. Une URM … Wikipédia en Français
register — I n. record, record book 1) to keep a register 2) a case; hotel register roster 3) the Social Register machine that registers the amount of each sale 4) a cash register II v. 1) (D; intr., tr.) to register as (she registered as a Republican; he… … Combinatory dictionary
Machine à registres illimités — En informatique, une machine à registres illimités ou URM (de l anglais : Unlimited Register Machine) est un modèle abstrait du fonctionnement des appareils mécaniques de calcul, tout comme les machines de Turing et le lambda calcul. Une URM … Wikipédia en Français
Machine code — or machine language is a system of impartible instructions executed directly by a computer s central processing unit. Each instruction performs a very specific task, typically either an operation on a unit of data (in a register or in memory, e.g … Wikipedia
Register — Reg is*ter (r?j ?s*t?r), n. [OE. registre, F. registre, LL. registrum,regestum, L. regesta, pl., fr. regerere, regestum, to carry back, to register; pref. re re + gerere to carry. See {Jest}, and cf. {Regest}.] 1. A written account or entry; an… … The Collaborative International Dictionary of English
Register renaming — In computer engineering, register renaming refers to a technique usedto avoid unnecessary serialization of program operations imposed by the reuseof registers by those operations.Problem definitionPrograms are composed of instructions which… … Wikipedia
Register allocation — In compiler optimization, register allocation is the process of multiplexing a large number of target program variables onto a small number of CPU registers. The goal is to keep as many operands as possible in registers to maximise the execution… … Wikipedia
register — register1 W3S3 [ˈredʒıstə US ər] n ▬▬▬▬▬▬▬ 1¦(official list)¦ 2¦(language style)¦ 3¦(music)¦ 4¦(machine)¦ 5¦(heating control)¦ ▬▬▬▬▬▬▬ [Date: 1300 1400; : Old French; Origin: registre, from Medieval Latin registrum, from Latin regerere to bring … Dictionary of contemporary English