Computing with Register Machines
E173600
"Computing with Register Machines" is a chapter in the classic computer science textbook *Structure and Interpretation of Computer Programs* that introduces low-level machine models and shows how higher-level language constructs can be implemented using simple register-based operations.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Computing with Register Machines canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1531281 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Computing with Register Machines Context triple: [Structure and Interpretation of Computer Programs, chapter, Computing with Register Machines]
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A.
On Computable Numbers with an Application to the Entscheidungsproblem
"On Computable Numbers, with an Application to the Entscheidungsproblem" is Alan Turing’s landmark 1936 paper that introduced the Turing machine model and founded the formal study of computability and the limits of algorithmic decision procedures.
-
B.
Introduction to the Theory of Computation
Introduction to the Theory of Computation is a widely used textbook in theoretical computer science that covers formal languages, automata, computability, and complexity theory.
-
C.
Turing machine
A Turing machine is an abstract computational model that manipulates symbols on an infinite tape according to a set of rules, providing a formal foundation for the concept of algorithm and computability.
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D.
Church–Turing thesis
The Church–Turing thesis is a foundational principle in computability theory stating that any function that can be effectively computed by an algorithm can be computed by a Turing machine (or equivalently by other formal models of computation).
-
E.
Blum complexity measures
Blum complexity measures are a formal framework in computational complexity theory that rigorously define and compare the resource usage (such as time or space) of algorithms via axiomatic conditions.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Computing with Register Machines Target entity description: "Computing with Register Machines" is a chapter in the classic computer science textbook *Structure and Interpretation of Computer Programs* that introduces low-level machine models and shows how higher-level language constructs can be implemented using simple register-based operations.
-
A.
On Computable Numbers with an Application to the Entscheidungsproblem
"On Computable Numbers, with an Application to the Entscheidungsproblem" is Alan Turing’s landmark 1936 paper that introduced the Turing machine model and founded the formal study of computability and the limits of algorithmic decision procedures.
-
B.
Introduction to the Theory of Computation
Introduction to the Theory of Computation is a widely used textbook in theoretical computer science that covers formal languages, automata, computability, and complexity theory.
-
C.
Turing machine
A Turing machine is an abstract computational model that manipulates symbols on an infinite tape according to a set of rules, providing a formal foundation for the concept of algorithm and computability.
-
D.
Church–Turing thesis
The Church–Turing thesis is a foundational principle in computability theory stating that any function that can be effectively computed by an algorithm can be computed by a Turing machine (or equivalently by other formal models of computation).
-
E.
Blum complexity measures
Blum complexity measures are a formal framework in computational complexity theory that rigorously define and compare the resource usage (such as time or space) of algorithms via axiomatic conditions.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
book chapter
ⓘ
educational text ⓘ |
| aimsTo |
bridge high-level programming and low-level machine models
ⓘ
show how interpreters and compilers can be built from simple components ⓘ |
| educationalLevel | university ⓘ |
| field |
computer architecture
ⓘ
computer science ⓘ programming languages ⓘ theoretical computer science ⓘ |
| focusesOn |
design of register machines for evaluators
ⓘ
how high-level constructs map to low-level operations ⓘ implementation of Scheme-like languages ⓘ relationship between interpreters and compilers ⓘ |
| hasAuthor |
Gerald Jay Sussman
ⓘ
Harold Abelson ⓘ
linked to:
Hal Abelson
Julie Sussman ⓘ |
| hasSubject |
abstraction barriers
ⓘ
compilation ⓘ control structures ⓘ data paths and controllers ⓘ environment model of evaluation ⓘ explicit-control evaluator ⓘ garbage collection ⓘ implementation of programming languages ⓘ instruction sequences ⓘ interpretation ⓘ low-level computation ⓘ machine models ⓘ metacircular evaluation ⓘ microcode ⓘ register machines ⓘ register-based operations ⓘ simulation of machines ⓘ stack discipline ⓘ state transitions ⓘ storage allocation ⓘ virtual machines ⓘ |
| inLanguage | English ⓘ |
| partOf | Structure and Interpretation of Computer Programs ⓘ |
| teaches |
how to design a register machine
ⓘ
how to express algorithms as machine instructions ⓘ how to implement control structures with jumps and branches ⓘ how to implement procedure calls with stacks ⓘ how to reason about machine state ⓘ |
| usesConcept |
conditional branching
ⓘ
labels and goto-like control ⓘ loops ⓘ operations ⓘ registers ⓘ sequencing ⓘ stacks ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Computing with Register Machines Description of subject: "Computing with Register Machines" is a chapter in the classic computer science textbook *Structure and Interpretation of Computer Programs* that introduces low-level machine models and shows how higher-level language constructs can be implemented using simple register-based operations.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.