“Macroscopic Data Structure Analysis and Optimization”
E457336
“Macroscopic Data Structure Analysis and Optimization” is Chris Lattner’s PhD thesis, in which he develops compiler techniques to analyze and optimize large-scale data structure usage for improved program performance.
All labels observed (1)
| Label | Occurrences |
|---|---|
| “Macroscopic Data Structure Analysis and Optimization” canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4654804 — 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: “Macroscopic Data Structure Analysis and Optimization” Context triple: [Chris Lattner, doctoralThesis, “Macroscopic Data Structure Analysis and Optimization”]
-
A.
The Future of Data Analysis
"The Future of Data Analysis" is a seminal 1962 paper by statistician John W. Tukey that helped define and popularize exploratory data analysis and reshaped modern statistical practice.
-
B.
Data-Driven Discovery Initiative
The Data-Driven Discovery Initiative is a research program that advances scientific discovery by supporting innovative data science methods, tools, and researchers across disciplines.
-
C.
Towards a New Architecture
Towards a New Architecture is a seminal 1923 architectural treatise by Le Corbusier that advocates for modernist design principles grounded in industrialization, functionalism, and new construction technologies.
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D.
Algorithms + Data Structures = Programs
"Algorithms + Data Structures = Programs" is a classic computer science textbook by Niklaus Wirth that systematically teaches how combining appropriate data structures with algorithms leads to effective and efficient programs.
-
E.
The niche: an abstractly inhabited hypervolume
"The niche: an abstractly inhabited hypervolume" is a seminal ecological paper by G. Evelyn Hutchinson that conceptualizes an organism’s niche as a multidimensional space defined by environmental conditions and resources.
- 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: “Macroscopic Data Structure Analysis and Optimization” Target entity description: “Macroscopic Data Structure Analysis and Optimization” is Chris Lattner’s PhD thesis, in which he develops compiler techniques to analyze and optimize large-scale data structure usage for improved program performance.
-
A.
The Future of Data Analysis
"The Future of Data Analysis" is a seminal 1962 paper by statistician John W. Tukey that helped define and popularize exploratory data analysis and reshaped modern statistical practice.
-
B.
Data-Driven Discovery Initiative
The Data-Driven Discovery Initiative is a research program that advances scientific discovery by supporting innovative data science methods, tools, and researchers across disciplines.
-
C.
Towards a New Architecture
Towards a New Architecture is a seminal 1923 architectural treatise by Le Corbusier that advocates for modernist design principles grounded in industrialization, functionalism, and new construction technologies.
-
D.
Algorithms + Data Structures = Programs
"Algorithms + Data Structures = Programs" is a classic computer science textbook by Niklaus Wirth that systematically teaches how combining appropriate data structures with algorithms leads to effective and efficient programs.
-
E.
The niche: an abstractly inhabited hypervolume
"The niche: an abstractly inhabited hypervolume" is a seminal ecological paper by G. Evelyn Hutchinson that conceptualizes an organism’s niche as a multidimensional space defined by environmental conditions and resources.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
PhD dissertation
ⓘ
doctoral thesis ⓘ |
| academicAdvisor | Vikram Adve ⓘ |
| academicDiscipline |
Computer Science
ⓘ
linked to:
computer science
|
| aimsTo |
automatically improve program performance
ⓘ
bridge gap between data structure design and low-level code generation ⓘ enable compilers to reason about high-level data structure usage ⓘ |
| author | Chris Lattner ⓘ |
| availableAs | PDF document ⓘ |
| citationType | academic thesis ⓘ |
| contributesTo |
LLVM project
ⓘ
linked to:
LLVM
|
| country |
United States
ⓘ
linked to:
United States of America
|
| degree | Doctor of Philosophy ⓘ |
| field |
compiler optimization
ⓘ
computer science ⓘ program analysis ⓘ programming languages ⓘ |
| focusesOn |
automatic optimization of data structures
ⓘ
improving cache behavior ⓘ improving runtime performance ⓘ large-scale data structure usage ⓘ macroscopic program behavior ⓘ reducing memory footprint ⓘ |
| hasComponent |
compiler optimization algorithms
ⓘ
experimental evaluation on benchmark programs ⓘ static analysis framework for data structures ⓘ |
| institution |
University of Illinois at Urbana-Champaign
ⓘ
linked to:
University of Illinois at Urbana–Champaign
|
| intendedAudience |
compiler researchers
ⓘ
graduate students in computer science ⓘ programming language researchers ⓘ |
| language | English ⓘ |
| relatedTo |
alias analysis
ⓘ
escape analysis ⓘ memory hierarchy optimization ⓘ object-oriented programs ⓘ profile-guided optimization ⓘ systems programming ⓘ |
| topic |
compiler transformations
ⓘ
data structure optimization ⓘ heap analysis ⓘ interprocedural analysis ⓘ pointer analysis ⓘ program performance optimization ⓘ shape analysis ⓘ static analysis ⓘ whole-program optimization ⓘ |
| uses |
LLVM compiler infrastructure
ⓘ
linked to:
LLVM
|
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: “Macroscopic Data Structure Analysis and Optimization” Description of subject: “Macroscopic Data Structure Analysis and Optimization” is Chris Lattner’s PhD thesis, in which he develops compiler techniques to analyze and optimize large-scale data structure usage for improved program performance.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.