Article

Data layouts for object-oriented programs

Author:

Martin HirzelAuthors Info & Claims

SIGMETRICS '07: Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems

Pages 265 - 276

https://doi.org/10.1145/1254882.1254915

Published: 12 June 2007 Publication History

Abstract

Object-oriented programs rely heavily on objects and pointers, making them vulnerable to slow downs from cache and TLB misses. The cache and TLB behavior depends on the data layout of objects in memory. There are many possible data layouts with different impacts on performance, but it is not known which perform better. This paper presents a novel framework for evaluating data layouts. The framework both makes implementing many layouts easy, andenables performance measurements of real programs using a product Java virtual machine on stock hardware. This is achieved by sorting objects during copying garbage collection; outside of garbage collection, program performance is solely determined by the data layout that the sort key implements. This paper surveys and evaluates 10 common data layouts with 32 realistic bench mark programs running on 3 different hardware configurations. The results confirm the importance of data layouts for program performance, and show that almost all layouts yield the best performance for some programs and the worst performance for others.

References

[1]

D. Abuaiadh, Y. Ossia, E. Petrank, and U. Silbershtein. An efficient parallel heap compaction algorithm. In OOPSLA, 2004.

Digital Library

[2]

A.-R. Adl-Tabatabai, R. L. Hudson, M. J. Serrano, and S. Subramoney. Prefetch injection based on hardware monitoring and object metadata. In PLDI, 2004.

Digital Library

[3]

E. D. Berger and B. G. Zorn. DieHard: Probabilistic memory safety for unsafe languages. In PLDI, 2006.

Digital Library

[4]

S. Bhatkar, R. Sekar, and D. C. DuVarney. Efficient techniques for comprehensive protection from memory error exploits. In USENIX Security Symposium, 2005.

Digital Library

[5]

S. M. Blackburn, P. Cheng, and K. S. McKinley. Myths and realities: The performance impact of garbage collection. In SIGMETRICS, 2004.

Digital Library

[6]

S. M. Blackburn, R. Garner, C. Hoffman, A. M. Khan, K. S. McKinley, R. Bentzur, A. Diwan, D. Feinberg, D. Frampton, S. Z. Guyer, M. Hirzel, A. Hosking, M. Jump, H. Lee, J. E. B. Moss, A. Phansalkar, D. Stefanović, T. VanDrunen, D. von Dincklage, and B. Wiedermann. The DaCapo benchmarks: Java benchmarking development and analysis. In OOPSLA, 2006.

Digital Library

[7]

S. M. Blackburn and K. S. McKinley. Ulterior reference counting: Fast garbage collection without a long wait. In OOPSLA, 2003.

Digital Library

[8]

R. Blau. Paging on an object-oriented personal computer. In SIGMETRICS, 1983.

Digital Library

[9]

H.-J. Boehm and M. Weiser. Garbage collection in an uncooperative environment. Software -- Practice and Experience (SPE), 1988.

Digital Library

[10]

S. Browne, J. Dongarra, N. Garner, K. London, and P. Mucci. A scalable cross-platform infrastructure for application performance tuning using hardware counters. In IEEE SuperComputing (SC), 2000.

Digital Library

[11]

B. Calder, C. Krintz, S. John, and T. Austin. Cache-conscious data placement. In ASPLOS, 1998.

Digital Library

[12]

W. K. Chen, S. Bhansali, T. Chilimbi, X. Gao, and W. Chuang. Profile-guided proactive garbage collection for locality optimization. In PLDI, 2006.

Digital Library

[13]

C. J. Cheney. A nonrecursive list compacting algorithm. CACM, 1970.

Digital Library

[14]

P. Cheng and G. E. Blelloch. A parallel, real-time garbage collector. In PLDI, 2001.

Digital Library

[15]

S. Cherem and R. Rugina. Region analysis and transformation for Java programs. In ISMM, 2004.

Digital Library

[16]

T. M. Chilimbi and J. R. Larus. Using generational garbage collection to implement cache-conscious data placement. In ISMM, 1998.

Digital Library

[17]

G. E. Collins. A method for overlapping and erasure of lists. CACM, 1960.

Digital Library

[18]

W. T. Comfort. Multiword list items. CACM, 1964.

Digital Library

[19]

R. Courts. Improving locality of reference in a garbage-collecting memory management system. CACM, 1988.

Digital Library

[20]

D. Detlefs, C. Flood, S. Heller, and T. Printezis. Garbage-first garbage collection. In ISMM, 2004.

Digital Library

[21]

C. Ding and K. Kennedy. Improving cache performance in dynamic applications through data and computation reorganization at run time. In PLDI, 1999.

Digital Library

[22]

A. Diwan, D. Tarditi, and J. E. B. Moss. Memory subsystem performance of programs with intensive heap allocation. ACM Transactions on Computer Systems (TOCS), 1995.

Digital Library

[23]

R. R. Fenichel and J. C. Yochelson. A LISP garbage-collector for virtual-memory computer systems. CACM, 1969.

Digital Library

[24]

C. H. Flood, D. Detlefs, N. Shavit, and X. Zhang. Parallel garbage collection for shared memory multiprocessors. In Java Virtual Machine Research and Technology Symposium (JVM), 2001.

Digital Library

[25]

D. Gay and A. Aiken. Memory management with explicit regions. In PLDI, 1998.

Digital Library

[26]

R. H. Halstead, Jr. Multilisp: A language for concurrent symbolic computation. TOPLAS, 1985.

Digital Library

[27]

B. Hayes. Using key object opportunism to collect old objects. In OOPSLA, 1991.

Digital Library

[28]

M. Hertz, Y. Feng, and E. D. Berger. Garbage collection without paging. In PLDI, 2005.

Digital Library

[29]

M. Hirzel, A. Diwan, and M. Hertz. Connectivity-based garbage collection. In OOPSLA, 2003.

Digital Library

[30]

M. Hirzel, J. Henkel, A. Diwan, and M. Hind. Understanding the connectivity of heap objects. In ISMM, 2002.

Digital Library

[31]

X. Huang, S. M. Blackburn, K. S. McKinley, J. E. B. Moss, Z. Wang, and P. Cheng. The garbage collection advantage: improving program locality. In OOPSLA, 2004.

Digital Library

[32]

R. L. Hudson and J. E. B. Moss. Incremental collection of mature objects. In International Workshop on Memory Management, 1992.

Digital Library

[33]

A. Imai and E. Tick. Evaluation of parallel copying garbage collection on a shared-memory multiprocessor. IEEE Transactions on Parallel and Distributed Systems, 1993.

Digital Library

[34]

T. Inagaki, T. Onodera, H. Komatsu, and T. Nakatani. Stride prefetching by dynamically inspecting objects. In PLDI, 2003.

Digital Library

[35]

R. Jones and R. Lins. Garbage collection: Algorithms for automatic dynamic memory management. John Wiley & Son Ltd., 1996.

Digital Library

[36]

T. Kotzmann and H. Mössenböck. Escape analysis in the context of dynamic compilation and deoptimization. In Virtual Execution Environments (VEE), 2005.

Digital Library

[37]

C. Lattner and V. Adve. Automatic pool allocation: Improving performance by controlling data structure layout on the heap. In PLDI, 2005.

Digital Library

[38]

H. Lieberman and C. Hewitt. A real-time garbage collector based on the lifetimes of objects. CACM, 1983.

Digital Library

[39]

P. McGachey and A. L. Hosking. Reducing generational copy reserve overhead with fallback compaction. In ISMM, 2006.

Digital Library

[40]

D. A. Moon. Garbage collection in a large Lisp system. In LISP and Functional Programming (LFP), 1984.

Digital Library

[41]

E. Petrank and D. Rawitz. The hardness of cache conscious data placement. In POPL, 2002.

Digital Library

[42]

F. Qian and L. Hendren. An adaptive, region-based allocator for Java. In ISMM, 2002.

Digital Library

[43]

M. B. Reinhold. Cache performance of garbage-collected programs. In PLDI, 1994.

Digital Library

[44]

S. Rubin, R. Bodik, and T. M. Chilimbi. An efficient profile-analysis framework for data layout optimizations. In POPL, 2002.

Digital Library

[45]

X. Shen, Y. Gao, C. Ding, and R. Archambault. Lightweight reference affinity analysis. In International Conference on Supercomputing (ICS), 2005.

Digital Library

[46]

X. Shen, Y. Zhong, and C. Ding. Locality phase prediction. In ASPLOS, 2004.

Digital Library

[47]

Y. Shuf, M. Gupta, R. Bordawekar, and J. P. Singh. Exploiting prolific types for memory management and optimizations. In POPL, 2002.

Digital Library

[48]

Y. Shuf, M. Gupta, H. Franke, A. Appel, and J. P. Singh. Creating and preserving locality of Java applications at allocation and garbage collection times. In OOPSLA, 2002.

Digital Library

[49]

Y. Shuf, M. J. Serrano, M. Gupta, and J. P. Singh. Characterizing the memory behavior of Java workloads: A structured view and opportunities for optimizations. In SIGMETRICS, 2001.

Digital Library

[50]

D. Siegwart and M. Hirzel. Improving locality with parallel hierarchical copying GC. In ISMM, 2006.

Digital Library

[51]

J. W. Stamos. Static grouping of small objects to enhance performance of a paged virtual memory. Transactions on Computer Systems (TOCS), 1984.

Digital Library

[52]

B. Steensgaard. Thread-specific heaps for multi-threaded programs. In ISMM, 2000.

Digital Library

[53]

P. F. Sweeney, M. Hauswirth, B. Cahoon, P. Cheng, A. Diwan, D. Grove, and M. Hind. Using hardware performance monitors to understand the behavior of Java applications. In Virtual Machine Research and Technology Symposium (VM), 2004.

Digital Library

[54]

M. Tofte. A brief introduction to regions. In ISMM, 1998.

Digital Library

[55]

D. Ungar. Generation scavenging: A non-disruptive high performance storage reclamation algorithm. In Software Engineering Symposium on Practical Software Development Environments (SESPSDE), 1984.

Digital Library

[56]

P. R. Wilson, M. S. Lam, and T. G. Moher. Effective "static-graph" reorganization to improve locality in a garbage-collected system. In Conference on PLDI, 1991.

Digital Library

[57]

C. Zhang, C. Ding, M. Ogihara, Y. Zhong, and Y. Wu. A hierarchical model of data locality. In POPL, 2006.

Digital Library

[58]

C. Zhang, K. Kelsey, X. Shen, C. Ding, M. Hertz, and M. Ogihara. Program-level adaptive memory management. In ISMM, 2006.

Digital Library

[59]

L. Zhang, Z. Fang, M. Parker, B. K. Mathew, L. Schaelicke, J. B. Carter, W. C. Hsieh, and S. A. McKee. The Impulse memory controller. IEEE Transactions on Computers, 2001.

Digital Library

[60]

Y. Zhong, M. Orlovich, X. Shen, and C. Ding. Array regrouping and structure splitting using whole-program reference affinity. In PLDI, 2004.

Digital Library

[61]

B. G. Zorn. The effect of garbage collection on cache performance. Technical report, University of Colorado at Boulder, 1991.

Cited By

Norlinder JÖsterlund EWrigstad T(2022)Compressed Forwarding Tables ReconsideredProceedings of the 19th International Conference on Managed Programming Languages and Runtimes10.1145/3546918.3546928(45-63)Online publication date: 14-Sep-2022
https://dl.acm.org/doi/10.1145/3546918.3546928
Olson MKammerdiener BJantz MDoshi KJones T(2022)Online Application Guidance for Heterogeneous Memory SystemsACM Transactions on Architecture and Code Optimization10.1145/353385519:3(1-27)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3533855
Esmaeili AGallagher JSpringer JMatson E(2022)HAMLET: A Hierarchical Agent-based Machine Learning PlatformACM Transactions on Autonomous and Adaptive Systems10.1145/353019116:3-4(1-46)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3530191
Show More Cited By

Index Terms

Data layouts for object-oriented programs
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
  2. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Memory management
        Garbage collection

Recommendations

Data layouts for object-oriented programs
SIGMETRICS '07 Conference Proceedings

Object-oriented programs rely heavily on objects and pointers, making them vulnerable to slow downs from cache and TLB misses. The cache and TLB behavior depends on the data layout of objects in memory. There are many possible data layouts with ...
Improving Performance of Large Physically Indexed Caches by Decoupling Memory Addresses from Cache Addresses

Modern CPUs often use large physically indexed caches that are direct-mapped or have low associativities. Such caches do not interact well with virtual memory systems. An improperly placed physical page will end up in a wrong place in the cache, causing ...
Reactive NUCA: near-optimal block placement and replication in distributed caches

Increases in on-chip communication delay and the large working sets of server and scientific workloads complicate the design of the on-chip last-level cache for multicore processors. The large working sets favor a shared cache design that maximizes the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMETRICS '07: Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems

June 2007

398 pages

ISBN:9781595936394

DOI:10.1145/1254882

General Chair:
Leana Golubchik
University of Southern California, USA
,
Program Chairs:
Mostafa Ammar
Georgia Institute of Technology, USA
,
Mor Harchol-Balter
Carnegie Mellon University, USA

ACM SIGMETRICS Performance Evaluation Review Volume 35, Issue 1
SIGMETRICS '07 Conference Proceedings
June 2007
382 pages
ISSN:0163-5999
DOI:10.1145/1269899
Issue’s Table of Contents

Copyright © 2007 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

SIGMETRICS07

Sponsor:

SIGMETRICS07: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

June 12 - 16, 2007

California, San Diego, USA

Acceptance Rates

Overall Acceptance Rate 459 of 2,691 submissions, 17%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
615
Total Downloads

Downloads (Last 12 months)24
Downloads (Last 6 weeks)4

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Norlinder JÖsterlund EWrigstad T(2022)Compressed Forwarding Tables ReconsideredProceedings of the 19th International Conference on Managed Programming Languages and Runtimes10.1145/3546918.3546928(45-63)Online publication date: 14-Sep-2022
https://dl.acm.org/doi/10.1145/3546918.3546928
Olson MKammerdiener BJantz MDoshi KJones T(2022)Online Application Guidance for Heterogeneous Memory SystemsACM Transactions on Architecture and Code Optimization10.1145/353385519:3(1-27)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3533855
Esmaeili AGallagher JSpringer JMatson E(2022)HAMLET: A Hierarchical Agent-based Machine Learning PlatformACM Transactions on Autonomous and Adaptive Systems10.1145/353019116:3-4(1-46)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3530191
Di KZhou YJiang JYan FYang SJiang Y(2022)Risk-aware Collection Strategies for Multirobot Foraging in Hazardous EnvironmentsACM Transactions on Autonomous and Adaptive Systems10.1145/351425116:3-4(1-38)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3514251
Zudaire SNahabedian LUchitel S(2022)Assured Mission Adaptation of UAVsACM Transactions on Autonomous and Adaptive Systems10.1145/351309116:3-4(1-27)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3513091
Silva RSobral J(2022)Efficient High-Level Programming in Plain JavaInternational Journal of Parallel Programming10.1007/s10766-022-00747-051:1(22-42)Online publication date: 5-Dec-2022
https://doi.org/10.1007/s10766-022-00747-0
Silva RSobral J(2022)High Performance Computing with Java StreamsEuro-Par 2021: Parallel Processing Workshops10.1007/978-3-031-06156-1_2(17-28)Online publication date: 9-Jun-2022
https://doi.org/10.1007/978-3-031-06156-1_2
Ismail MSuh GMars JTang LXue JWu P(2020)Efficient nursery sizing for managed languages on multi-core processors with shared cachesProceedings of the 18th ACM/IEEE International Symposium on Code Generation and Optimization10.1145/3368826.3377908(1-15)Online publication date: 22-Feb-2020
https://dl.acm.org/doi/10.1145/3368826.3377908
Effler TKammerdiener BJantz MSengupta SKulkarni PDoshi KJones T(2019)Evaluating the effectiveness of program data features for guiding memory managementProceedings of the International Symposium on Memory Systems10.1145/3357526.3357537(383-395)Online publication date: 30-Sep-2019
https://dl.acm.org/doi/10.1145/3357526.3357537
Franco JHagelin MWrigstad TDrossopoulou SEisenbach STorlak EStorm TBiddle R(2017)You can have it all: abstraction and good cache performanceProceedings of the 2017 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software10.1145/3133850.3133861(148-167)Online publication date: 25-Oct-2017
https://dl.acm.org/doi/10.1145/3133850.3133861
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