research-article

Adapton: composable, demand-driven incremental computation

Authors:

Matthew A. Hammer,

Khoo Yit Phang,

Jeffrey S. FosterAuthors Info & Claims

PLDI '14: Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 156 - 166

https://doi.org/10.1145/2594291.2594324

Published: 09 June 2014 Publication History

Abstract

Many researchers have proposed programming languages that support incremental computation (IC), which allows programs to be efficiently re-executed after a small change to the input. However, existing implementations of such languages have two important drawbacks. First, recomputation is oblivious to specific demands on the program output; that is, if a program input changes, all dependencies will be recomputed, even if an observer no longer requires certain outputs. Second, programs are made incremental as a unit, with little or no support for reusing results outside of their original context, e.g., when reordered.

To address these problems, we present λ_ic^cdd, a core calculus that applies a demand-driven semantics to incremental computation, tracking changes in a hierarchical fashion in a novel demanded computation graph. λ_ic^cdd also formalizes an explicit separation between inner, incremental computations and outer observers. This combination ensures λ_ic^cdd programs only recompute computations as demanded by observers, and allows inner computations to be reused more liberally. We present Adapton, an OCaml library implementing λ_ic^cdd. We evaluated Adapton on a range of benchmarks, and found that it provides reliable speedups, and in many cases dramatically outperforms state-of-the-art IC approaches.

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Laddad SCheung AHellerstein JMilano M(2025)Flo: A Semantic Foundation for Progressive Stream ProcessingProceedings of the ACM on Programming Languages10.1145/37048459:POPL(241-270)Online publication date: 9-Jan-2025
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Wang ZPailoor SPrakash AWang YDillig I(2024)From Batch to Stream: Automatic Generation of Online AlgorithmsProceedings of the ACM on Programming Languages10.1145/36564188:PLDI(1014-1039)Online publication date: 20-Jun-2024
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Stein BChang BSridharan M(2024)Interactive Abstract Interpretation with Demanded SummarizationACM Transactions on Programming Languages and Systems10.1145/364844146:1(1-40)Online publication date: 15-Feb-2024
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Index Terms

Adapton: composable, demand-driven incremental computation
1. Software and its engineering
  1. Software notations and tools
    1. Formal language definitions
    2. General programming languages
      1. Language features
2. Theory of computation
  1. Formal languages and automata theory
  2. Semantics and reasoning
    1. Program semantics

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Published In

cover image ACM Conferences

PLDI '14: Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2014

619 pages

ISBN:9781450327848

DOI:10.1145/2594291

General Chair:
Michael O'Boyle
University of Edinburgh
,
Program Chair:
Keshav Pingali
University of Texas, Austin

ACM SIGPLAN Notices Volume 49, Issue 6
PLDI '14
June 2014
598 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2666356
Editor:
Andy Gill
University of Kansas, Lawrence, KS
Issue’s Table of Contents

Copyright © 2014 ACM.

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Published: 09 June 2014

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PLDI '14

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PLDI '14: ACM SIGPLAN Conference on Programming Language Design and Implementation

June 9 - 11, 2014

Edinburgh, United Kingdom

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PLDI '14 Paper Acceptance Rate 52 of 287 submissions, 18%;

Overall Acceptance Rate 406 of 2,067 submissions, 20%

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https://dl.acm.org/doi/10.1145/3704845
Wang ZPailoor SPrakash AWang YDillig I(2024)From Batch to Stream: Automatic Generation of Online AlgorithmsProceedings of the ACM on Programming Languages10.1145/36564188:PLDI(1014-1039)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3656418
Stein BChang BSridharan M(2024)Interactive Abstract Interpretation with Demanded SummarizationACM Transactions on Programming Languages and Systems10.1145/364844146:1(1-40)Online publication date: 15-Feb-2024
https://dl.acm.org/doi/10.1145/3648441
Nahvy ANavabi Z(2024)Pico-Programmable Neurons to Reduce Computations for Deep Neural Network AcceleratorsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.338669832:7(1216-1227)Online publication date: Jul-2024
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Lei ZShi XLv CYu XZhao X(2023)ChituProceedings of the 2023 ACM Symposium on Cloud Computing10.1145/3620678.3624794(597-610)Online publication date: 30-Oct-2023
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Zwaan Avan Antwerpen HVisser E(2022)Incremental type-checking for free: using scope graphs to derive incremental type-checkersProceedings of the ACM on Programming Languages10.1145/35633036:OOPSLA2(424-448)Online publication date: 31-Oct-2022
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Li YSatya KZhang Q(2022)Efficient algorithms for dynamic bidirected Dyck-reachabilityProceedings of the ACM on Programming Languages10.1145/34987246:POPL(1-29)Online publication date: 12-Jan-2022
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Stein BChang BSridharan MFreund SYahav E(2021)Demanded abstract interpretationProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454044(282-295)Online publication date: 19-Jun-2021
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Gao KNojima TYu HYang Y(2020)Trident: Toward Distributed Reactive SDN Programming With Consistent UpdatesIEEE Journal on Selected Areas in Communications10.1109/JSAC.2020.299965438:7(1322-1334)Online publication date: Jul-2020
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Voelter MBirken KLisson SRimer ANierstrasz OGray JOliveira B(2019)Shadow models: incremental transformations for MPSProceedings of the 12th ACM SIGPLAN International Conference on Software Language Engineering10.1145/3357766.3359528(61-65)Online publication date: 20-Oct-2019
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