Article

An experimental analysis of self-adjusting computation

Authors:

Guy E. Blelloch,

Matthias Blume,

Kanat TangwongsanAuthors Info & Claims

PLDI '06: Proceedings of the 27th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 96 - 107

https://doi.org/10.1145/1133981.1133993

Published: 11 June 2006 Publication History

Abstract

Dependence graphs and memoization can be used to efficiently update the output of a program as the input changes dynamically. Recent work has studied techniques for combining these approaches to effectively dynamize a wide range of applications. Toward this end various theoretical results were given. In this paper we describe the implementation of a library based on these ideas, and present experimental results on the efficiency of this library on a variety of applications. The results of the experiments indicate that the approach is effective in practice, often requiring orders of magnitude less time than recomputing the output from scratch. We believe this is the first experimental evidence that incremental computation of any type is effective in practice for a reasonably broad set of applications.

References

[1]

M. Abadi, B. W. Lampson, and J.-J. Levy. Analysis and caching of dependencies. In International Conference on Functional Programming, pages 83--91, 1996.

Digital Library

[2]

U. A. Acar. Self-Adjusting Computation. PhD thesis, Department of Computer Science, Carnegie Mellon University, May 2005.

Digital Library

[3]

U. A. Acar, G. E. Blelloch, M. Blume, R. Harper, and K. Tangwongsan. A library for self-adjusting computation. In ACM SIGPLAN Workshop on ML, 2005.

[4]

U. A. Acar, G. E. Blelloch, and R. Harper. Adaptive functional programming. In Proceedings of the 29th Annual ACM Symposium on Principles of Programming Languages, pages 247--259, 2002.

Digital Library

[5]

U. A. Acar, G. E. Blelloch, and R. Harper. Selective memoization. In Proceedings of the 30th Annual ACM Symposium on Principles of Programming Languages, 2003.

Digital Library

[6]

U. A. Acar, G. E. Blelloch, R. Harper, J. L. Vittes, and M. Woo. Dynamizing static algorithms with applications to dynamic trees and history independence. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2004.

Digital Library

[7]

U. A. Acar, G. E. Blelloch, K. Tangwongsan, and J. Vittes. Kinetic algorithms via self-adjusting computation. Technical Report CMU-CS-06-115, Department of Computer Science, Carnegie Mellon University, March 2006.

Digital Library

[8]

U. A. Acar, G. E. Blelloch, and J. L. Vittes. An experimental analysis of change propagation in dynamic trees. In Workshop on Algorithm Engineering and Experimentation, 2005.

[9]

C. B. Barber, D. P. Dobkin, and H. Huhdanpaa. The quickhull algorithm for convex hulls. ACM Trans. Math. Softw., 22(4):469--483, 1996.

Digital Library

[10]

R. Bellman. Dynamic Programming. Princeton University Press, 1957.

Digital Library

[11]

M. A. Bender, R. Cole, E. D. Demaine, M. Farach-Colton, and J. Zito. Two simplified algorithms for maintaining order in a list. In Lecture Notes in Computer Science, pages 152--164, 2002.

Digital Library

[12]

G. S. Brodal and R. Jacob. Dynamic planar convex hull. In Proceedings of the 43rd Annual IEEE Symposium on Foundations of Computer Science, pages 617--626, 2002.

Digital Library

[13]

M. Carlsson. Monads for incremental computing. In Proceedings of the seventh ACM SIGPLAN international conference on Functional programming, pages 26--35. ACM Press, 2002.

Digital Library

[14]

T. M. Chan. Optimal output-sensitive convex hull algorithms in two and three dimensions. Discrete and Computational Geometry, 16:361--368, 1996.

Digital Library

[15]

T. M. Chan. Dynamic planar convex hull operations in near-logarithmic amortized time. J. ACM, 48(1):1--12, 2001.

Digital Library

[16]

Y.-J. Chiang and R. Tamassia. Dynamic algorithms in computational geometry. Proceedings of the IEEE, 80(9):1412--1434, 1992.

[17]

A. Demers, T. Reps, and T. Teitelbaum. Incremental evaluation of attribute grammars with application to syntax directed editors. In Proceedings of the 8th Annual ACM Symposium on Principles of Programming Languages, pages 105--116, 1981.

Digital Library

[18]

P. F. Dietz and D. D. Sleator. Two algorithms for maintaining order in a list. In Proceedings of the 19th ACM Symposium on Theory of Computing, pages 365--372, 1987.

Digital Library

[19]

D. Eppstein, Z. Galil, and G. F. Italiano. Dynamic graph algorithms. In M. J. Atallah, editor, Algorithms and Theory of Computation Handbook, chapter 8. CRC Press, 1999.

[20]

R. L. Graham. An efficient algorithm for determining the convex hull of a finite planar set. Information Processing Letters, 1:132--133, 1972.

[21]

A. Heydon, R. Levin, and Y. Yu. Caching function calls using precise dependencies. In Proceedings of the 2000 ACM SIGPLAN Conference on PLDI, pages 311--320, May 2000.

Digital Library

[22]

D. G. Kirkpatrick and R. Seidel. The ultimate planar convex hull algorithm. SIAM Journal on Computing, 15(2):287--299, 1986.

Digital Library

[23]

D. Knuth. The Art of Computer Programming: Sorting and Searching. Addison Wesley, 1998.

Digital Library

[24]

Y. A. Liu, S. Stoller, and T. Teitelbaum. Discovering auxiliary information for incremental computation. In Proceedings of the 23rd Annual ACM Symposium on Principles of Programming Languages, pages 157--170, 1996.

Digital Library

[25]

Y. A. Liu, S. Stoller, and T. Teitelbaum. Static caching for incremental computation. ACM Transactions on Programming Languages and Systems, 20(3):546--585, 1998.

Digital Library

[26]

J. McCarthy. A Basis for a Mathematical Theory of Computation. In P. Braffort and D. Hirschberg, editors, Computer Programming and Formal Systems, pages 33- 70. North-Holland, Amsterdam, 1963.

[27]

D. Michie. Memo functions and machine learning. Nature, 218:19--22, 1968.

[28]

M. H. Overmans and J. van Leeuwen. Maintenance of configurations in the plane. Journal of Computer and System Sciences, 23:166--204, 1981.

[29]

W. Pugh. Incremental computation via function caching. PhD thesis, Department of Computer Science, Cornell University, August 1988.

[30]

W. Pugh and T. Teitelbaum. Incremental computation via function caching. In Proceedings of the 16th Annual ACM Symposium on Principles of Programming Languages, pages 315--328, 1989.

Digital Library

[31]

G. Ramalingam and T. Reps. A categorized bibliography on incremental computation. In Conference Record of the 20th Annual ACM Symposium on POPL, pages 502--510, Jan. 1993.

Digital Library

[32]

R. Seidel. Linear programming and convex hulls made easy. In SCG '90: Proceedings of the sixth annual symposium on Computational geometry, pages 211--215, New York, NY, USA, 1990. ACM Press.

Digital Library

[33]

M. I. Shamos. Computational Geometry. PhD thesis, Department of Computer Science, Yale University, 1978.

Digital Library

[34]

R. Wenger. Randomized quickhull. Algorithmica, 17(3):322--329, 1997.

Cited By

Wehr SSperber MHutton G(2023)A Software Architecture Based on Coarse-Grained Self-Adjusting ComputationsProceedings of the 1st ACM SIGPLAN International Workshop on Functional Software Architecture10.1145/3609025.3609481(1-10)Online publication date: 30-Aug-2023
https://dl.acm.org/doi/10.1145/3609025.3609481
Liu BHuang J(2018)D4: fast concurrency debugging with parallel differential analysisACM SIGPLAN Notices10.1145/3296979.319239053:4(359-373)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3296979.3192390
Liu BHuang JFoster JGrossman D(2018)D4: fast concurrency debugging with parallel differential analysisProceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/3192366.3192390(359-373)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3192366.3192390
Show More Cited By

Index Terms

An experimental analysis of self-adjusting computation
1. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features

Recommendations

An experimental analysis of self-adjusting computation

Recent work on adaptive functional programming (AFP) developed techniques for writing programs that can respond to modifications to their data by performing change propagation. To achieve this, executions of programs are represented with dynamic ...
An experimental analysis of self-adjusting computation
Proceedings of the 2006 PLDI Conference

Dependence graphs and memoization can be used to efficiently update the output of a program as the input changes dynamically. Recent work has studied techniques for combining these approaches to effectively dynamize a wide range of applications. Toward ...
Efficient Parallel Self-Adjusting Computation
SPAA '21: Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures

Self-adjusting computation is an approach for automatically producing dynamic algorithms from static ones. It works by tracking control and data dependencies, and propagating changes through the dependencies when making an update. Extensively studied in ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

PLDI '06: Proceedings of the 27th ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2006

438 pages

ISBN:1595933204

DOI:10.1145/1133981

General Chair:
Michael Schwartzbach
University of Aarhus, Denmark
,
Program Chair:
Thomas Ball
Microsoft Research

ACM SIGPLAN Notices Volume 41, Issue 6
Proceedings of the 2006 PLDI Conference
June 2006
426 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/1133255
Issue’s Table of Contents

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

In-Cooperation

SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 June 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

PLDI06

Sponsor:

PLDI06: ACM SIGPLAN Conference on Programming Language Design and Implementation 2006

June 11 - 14, 2006

Ontario, Ottawa, Canada

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

43
Total Citations
View Citations
572
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)1

Reflects downloads up to 09 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wehr SSperber MHutton G(2023)A Software Architecture Based on Coarse-Grained Self-Adjusting ComputationsProceedings of the 1st ACM SIGPLAN International Workshop on Functional Software Architecture10.1145/3609025.3609481(1-10)Online publication date: 30-Aug-2023
https://dl.acm.org/doi/10.1145/3609025.3609481
Liu BHuang J(2018)D4: fast concurrency debugging with parallel differential analysisACM SIGPLAN Notices10.1145/3296979.319239053:4(359-373)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3296979.3192390
Liu BHuang JFoster JGrossman D(2018)D4: fast concurrency debugging with parallel differential analysisProceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/3192366.3192390(359-373)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3192366.3192390
Bhatotia PFonseca PAcar UBrandenburg BRodrigues R(2015)iThreadsACM SIGARCH Computer Architecture News10.1145/2786763.269437143:1(645-659)Online publication date: 14-Mar-2015
https://dl.acm.org/doi/10.1145/2786763.2694371
Bhatotia PFonseca PAcar UBrandenburg BRodrigues R(2015)iThreadsACM SIGPLAN Notices10.1145/2775054.269437150:4(645-659)Online publication date: 14-Mar-2015
https://dl.acm.org/doi/10.1145/2775054.2694371
Bhatotia PFonseca PAcar UBrandenburg BRodrigues ROzturk OEbcioglu KDwarkadas S(2015)iThreadsProceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/2694344.2694371(645-659)Online publication date: 14-Mar-2015
https://dl.acm.org/doi/10.1145/2694344.2694371
Orchard DPetricek T(2014)Embedding effect systems in HaskellACM SIGPLAN Notices10.1145/2775050.263336849:12(13-24)Online publication date: 3-Sep-2014
https://dl.acm.org/doi/10.1145/2775050.2633368
Pike L(2014)SmartCheckACM SIGPLAN Notices10.1145/2775050.263336549:12(53-64)Online publication date: 3-Sep-2014
https://dl.acm.org/doi/10.1145/2775050.2633365
Wu NSchrijvers THinze R(2014)Effect handlers in scopeACM SIGPLAN Notices10.1145/2775050.263335849:12(1-12)Online publication date: 3-Sep-2014
https://dl.acm.org/doi/10.1145/2775050.2633358
Hammer MPhang KHicks MFoster J(2014)AdaptonACM SIGPLAN Notices10.1145/2666356.259432449:6(156-166)Online publication date: 9-Jun-2014
https://dl.acm.org/doi/10.1145/2666356.2594324
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents