Lambda-splitting: A higher-order approach to cross-module optimizations

Matthias Blume; Andrew Wilson Appel

Lambda-splitting: A higher-order approach to cross-module optimizations

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We describe an algorithm for automatic inline expansion across module boundaries that works in the presence of higher-order functions and free variables; it rearranges bindings and scopes as necessary to move nonexpansive code from one module to another. We describe - and implement - the algorithm as transformations on λ-calculus. Our inliner interacts well with separate compilation and is efficient, robust, and practical enough for everyday use in the SML/NJ compiler. Inlining improves performance by 4-8% on existing code, and makes it possible to use much more data abstraction by consistently eliminating penalties for modularity.

Original language	English (US)
Title of host publication	Proceedings of the ACM SIGPLAN International Conference on Functional Programming, ICFP
Editors	Anon
Publisher	ACM
Pages	112-124
Number of pages	13
State	Published - Jan 1 1997
Event	Proceedings of the 1997 ACM SIGPLAN International Conference on Functional Programming, ICFP - Amsterdam, Neth Duration: Jun 9 1997 → Jun 11 1997

Other

Other	Proceedings of the 1997 ACM SIGPLAN International Conference on Functional Programming, ICFP
City	Amsterdam, Neth
Period	6/9/97 → 6/11/97

All Science Journal Classification (ASJC) codes

Computer Science(all)

Cite this

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title = "Lambda-splitting: A higher-order approach to cross-module optimizations",

abstract = "We describe an algorithm for automatic inline expansion across module boundaries that works in the presence of higher-order functions and free variables; it rearranges bindings and scopes as necessary to move nonexpansive code from one module to another. We describe - and implement - the algorithm as transformations on λ-calculus. Our inliner interacts well with separate compilation and is efficient, robust, and practical enough for everyday use in the SML/NJ compiler. Inlining improves performance by 4-8% on existing code, and makes it possible to use much more data abstraction by consistently eliminating penalties for modularity.",

author = "Matthias Blume and Appel, {Andrew Wilson}",

year = "1997",

month = jan,

day = "1",

language = "English (US)",

pages = "112--124",

editor = "Anon",

booktitle = "Proceedings of the ACM SIGPLAN International Conference on Functional Programming, ICFP",

publisher = "ACM",

note = "Proceedings of the 1997 ACM SIGPLAN International Conference on Functional Programming, ICFP ; Conference date: 09-06-1997 Through 11-06-1997",

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T1 - Lambda-splitting

T2 - Proceedings of the 1997 ACM SIGPLAN International Conference on Functional Programming, ICFP

AU - Blume, Matthias

AU - Appel, Andrew Wilson

PY - 1997/1/1

Y1 - 1997/1/1

N2 - We describe an algorithm for automatic inline expansion across module boundaries that works in the presence of higher-order functions and free variables; it rearranges bindings and scopes as necessary to move nonexpansive code from one module to another. We describe - and implement - the algorithm as transformations on λ-calculus. Our inliner interacts well with separate compilation and is efficient, robust, and practical enough for everyday use in the SML/NJ compiler. Inlining improves performance by 4-8% on existing code, and makes it possible to use much more data abstraction by consistently eliminating penalties for modularity.

AB - We describe an algorithm for automatic inline expansion across module boundaries that works in the presence of higher-order functions and free variables; it rearranges bindings and scopes as necessary to move nonexpansive code from one module to another. We describe - and implement - the algorithm as transformations on λ-calculus. Our inliner interacts well with separate compilation and is efficient, robust, and practical enough for everyday use in the SML/NJ compiler. Inlining improves performance by 4-8% on existing code, and makes it possible to use much more data abstraction by consistently eliminating penalties for modularity.

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UR - http://www.scopus.com/inward/citedby.url?scp=0030697597&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0030697597

SP - 112

EP - 124

BT - Proceedings of the ACM SIGPLAN International Conference on Functional Programming, ICFP

A2 - Anon, null

PB - ACM

Y2 - 9 June 1997 through 11 June 1997

ER -

Lambda-splitting: A higher-order approach to cross-module optimizations

Abstract

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All Science Journal Classification (ASJC) codes

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Cite this