Article

Automatic pool allocation: improving performance by controlling data structure layout in the heap

Authors:

Vikram AdveAuthors Info & Claims

PLDI '05: Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation

Pages 129 - 142

https://doi.org/10.1145/1065010.1065027

Published: 12 June 2005 Publication History

Abstract

This paper describes Automatic Pool Allocation, a transformation framework that segregates distinct instances of heap-based data structures into seperate memory pools and allows heuristics to be used to partially control the internal layout of those data structures. The primary goal of this work is performance improvement, not automatic memory management, and the paper makes several new contributions. The key contribution is a new compiler algorithm for partitioning heap objects in imperative programs based on a context-sensitive pointer analysis, including a novel strategy for correct handling of indirect (and potentially unsafe) function calls. The transformation does not require type safe programs and works for the full generality of C and C++. Second, the paper describes several optimizations that exploit data structure partitioning to further improve program performance. Third, the paper evaluates how memory hierarchy behavior and overall program performance are impacted by the new transformations. Using a number of benchmarks and a few applications, we find that compilation times are extremely low, and overall running times for heap intensive programs speed up by 10-25% in many cases, about 2x in two cases, and more than 10x in two small benchmarks. Overall, we believe this work provides a new framework for optimizing pointer intensive programs by segregating and controlling the layout of heap-based data structures.

References

[1]

A. Aiken, M. F�hndrich, and R. Levien. Better static memory management: Improving region-based analysis of higher-order languages. In PLDI, pages 174--185, June 1995.]]

Digital Library

[2]

T. Austin, et al. The Pointer-intensive Benchmark Suite. www.cs.wisc.edu/~austin/ptr-dist.html+, Sept 1995.]]

[3]

A. Ayers, S. de Jong, J. Peyton, and R. Schooler. Scalable cross-module optimization. In PLDI, Montreal, June 1998.]]

Digital Library

[4]

D. A. Barrett and B. G. Zorn. Using lifetime predictors to improve memory allocation performance. In PLDI, pages 187--196, Albuquerque, New Mexixo, June 1993.]]

Digital Library

[5]

E. D. Berger, B. G. Zorn, and K. S. McKinley. Reconsidering custom memory allocation. In OOPSLA, Seattle, Washington, Nov. 2002.]]

Digital Library

[6]

B. Blanchet. Escape Analysis for Java(TM): Theory and Practice. TOPLAS, 25(6):713--775, Nov 2003.]]

Digital Library

[7]

G. Bollella and J. Gosling. The real-time specification for Java. Computer, 33(6):47--54, 2000.]]

Digital Library

[8]

C. Boyapati, A. Salcianu, W. Beebee, and M. Rinard. Ownership types for safe region-based memory management in real-time java. In PLDI, 2003.]]

Digital Library

[9]

B. Calder, K. Chandra, S. John, and T. Austin. Cache-conscious data placement. In Proc. ASPLOS-VIII, pages 139--149, San Jose, USA, 1998.]]

Digital Library

[10]

S. Cherem and R. Rugina. Region analysis and transformation for java programs. In 2004 Int'l Symposium On Memory Management, Vancouver, Canada, Oct. 2004.]]

Digital Library

[11]

T. M. Chilimbi, B. Davidson, and J. R. Larus. Cache-conscious structure definition. In PLDI'99, pages 13--24. ACM Press, 1999.]]

Digital Library

[12]

T. M. Chilimbi, M. D. Hill, and J. R. Larus. Cache-conscious structure layout. In PLDI'99, pages 1--12. ACM Press, 1999.]]

Digital Library

[13]

T. M. Chilimbi and J. R. Larus. Using generational garbage collection to implement cache-conscious data placement. ACM SIGPLAN Notices, 34(3):37--48, 1999.]]

Digital Library

[14]

W.-N. Chin, F. Craciun, S. Qin, and M. Rinard. Region inference for an object-oriented language. In PLDI, Washington, DC, June 2004.]]

Digital Library

[15]

R. Courts. Improving locality of reference in a garbage-collecting memory management system. CACM, 31(9):1128--1138, 1988.]]

Digital Library

[16]

M. Das. Unification-based pointer analysis with directional assignments. In PLDI, pages 35--46, 2000.]]

Digital Library

[17]

R. DeLine and M. F�hndrich. Enforcing high-level protocols in low-level software. In PLDI, Snowbird, UT, June 2001.]]

Digital Library

[18]

A. Demers, M. Weiser, B. Hayes, H. Boehm, D. Bobrow, and S. Shenker. Combining generational and conservative garbage collection: framework and implementations. In Proc. ACM POPL, pages 261--269, 1990.]]

Digital Library

[19]

D. Dhurjati, S. Kowshik, V. Adve, and C. Lattner. Memory safety without garbage collection for embedded applications. Transactions on Embedded Computing Systems, 4(1):73--111, Feb. 2005.]]

Digital Library

[20]

M. F�hndrich, J. Rehof, and M. Das. Scalable context-sensitive flow analysis using instantiation constraints. In PLDI, Vancouver, Canada, June 2000.]]

Digital Library

[21]

D. Gay and A. Aiken. Memory management with explicit regions. In PLDI, pages 313--323, Montreal, Canada, 1998.]]

Digital Library

[22]

D. Grossman, G. Morrisett, T. Jim, M. Hicks, Y. Wang, and J. Cheney. Region-based memory management in cyclone. In PLDI, June 2002.]]

Digital Library

[23]

D. Grunwald and B. Zorn. Customalloc: Efficient synthesized memory allocators. SP&E, 23(8):851--869, 1993.]]

Digital Library

[24]

N. Hallenberg, M. Elsman, and M. Tofte. Combining region inference and garbage collection. In PLDI, Berlin, Germany, June 2002.]]

Digital Library

[25]

J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In SIGMOD, pages 1--12, 2000.]]

Digital Library

[26]

D. R. Hanson. Fast allocation and deallcoation of memory based on object lifetimes. SP&E, 20(1):5--12, Jan 1990.]]

Digital Library

[27]

M. Hind. Pointer analysis: haven't we solved this problem yet? In PASTE, pages 54--61. ACM Press, 2001.]]

Digital Library

[28]

M. Hirzel, A. Diwan, and M. Hertz. Connectivity-based garbage collection. In OOPSLA, pages 359--373, 2003.]]

Digital Library

[29]

X. Huang, S. Blackburn, K. McKinley, E. Moss, Z. Wang, and P. Cheng. The garbage collection advantage: improving program locality. In OOPSLA, pages 69--80, 2004.]]

Digital Library

[30]

T. Jim, G. Morrisett, D. Grossman, M. Hicks, J. Cheney, and Y. Wang. Cyclone: A safe dialect of C. In USENIX Annual Technical Conference, Monterey, CA, 2002.]]

Digital Library

[31]

R. Jones. Garbage Collection. Algorithms for Automatic Dynamic Memory Management. John Wiley & Sons, 1999.]]

Digital Library

[32]

C. Lattner. Macroscopic Data Structure Analysis and Optimization. PhD thesis, Computer Science Dept., University of Illinois at Urbana-Champaign, Urbana, IL, May 2005. See http://llvm.cs.uiuc.edu.]]

Digital Library

[33]

C. Lattner and V. Adve. Automatic Pool Allocation for Disjoint Data Structures. In MSP, Berlin, Germany, Jun 2002.]]

Digital Library

[34]

C. Lattner and V. Adve. LLVM: A Compilation Framework for Lifelong Program Analysis and Transformation. In CGO, San Jose, USA, Mar 2004.]]

Digital Library

[35]

C. Lattner and V. Adve. Transparent Pointer Compression for Linked Data Structures. In Proc. ACM Workshop on Memory System Performance, Chicago, IL, Jun 2005.]]

Digital Library

[36]

D. Liang and M. J. Harrold. Efficient points-to analysis for whole-program analysis. In ESEC SIGSOFT FSE, pages 199--215, 1999.]]

Digital Library

[37]

D. Liang and M. J. Harrold. Efficient computation of parameterized pointer information for interprocedural analysis. In SAS, July 2001.]]

Digital Library

[38]

E. M. Nystrom, H.-S. Kim, and W. mei W. Hwu. Bottom-up and top-down context-sensitive summary-based pointer analysis. In SAS, 2004.]]

[39]

A. Rogers, M. Carlisle, J. Reppy, and L. Hendren. Supporting dynamic data structures on distributed memory machines. TOPLAS, 17(2), Mar. 1995.]]

Digital Library

[40]

P. Rundberg and F. Warg. The FreeBench v1.0 Benchmark Suite. http://www.freebench.org+, Jan 2002.]]

[41]

M. L. Seidl and B. G. Zorn. Segregating heap objects by reference behavior and lifetime. In ASPLOS-VIII, pages 12--23, San Jose, USA, 1998.]]

Digital Library

[42]

R. Shaham, E. Yahav, E. K. Kolodner, and M. Sagiv. Establishing local temporal heap safety properties with applications to compile-time memory management. In SAS, San Diego, USA, June 2003.]]

Digital Library

[43]

B. Steensgaard. Points-to analysis in almost linear time. In POPL, pages 32--41, Jan 1996.]]

Digital Library

[44]

M. Tofte and L. Birkedal. A region inference algorithm. TOPLAS, 20(4):724--768, July 1998.]]

Digital Library

[45]

M. Tofte and J.-P. Talpin. Implementation of the typed call-by-value λ-calculus using a stack of regions. In POPL, pages 188--201, 1994.]]

Digital Library

[46]

C. B. Zilles. Benchmark health considered harmful. SIGARCH Comput. Archit. News, 29(3):4--5, 2001.]]

Digital Library

Cited By

Salvador Rohwedder CL. De Carvalho JAmaral JRodríguez GSadayappan PSukumaran-Rajam A(2024)Region-Based Data Layout via Data Reuse AnalysisProceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction10.1145/3640537.3641571(49-59)Online publication date: 17-Feb-2024
https://dl.acm.org/doi/10.1145/3640537.3641571
Wanninger NMcMichen TCampanoni SDinda PTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)Getting a Handle on Unmanaged MemoryProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 310.1145/3620666.3651326(448-463)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620666.3651326
Tauro BSuchy BCampanoni SDinda PHale KTsafrir DMUSUVATHI MGupta RAbu-Ghazaleh N(2024)TrackFM: Far-out Compiler Support for a Far Memory WorldProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3617232.3624856(401-419)Online publication date: 27-Apr-2024
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Index Terms

Automatic pool allocation: improving performance by controlling data structure layout in the heap
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

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cover image ACM Conferences

PLDI '05: Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation

June 2005

338 pages

ISBN:1595930566

DOI:10.1145/1065010

General Chair:
Vivek Sarkar
IBM Research
,
Program Chair:
Mary Hall
USC/ISI

ACM SIGPLAN Notices Volume 40, Issue 6
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
June 2005
325 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/1064978
Issue’s Table of Contents

Copyright © 2005 ACM.

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Published: 12 June 2005

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https://dl.acm.org/doi/10.1145/3640537.3641571
Wanninger NMcMichen TCampanoni SDinda PTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)Getting a Handle on Unmanaged MemoryProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 310.1145/3620666.3651326(448-463)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620666.3651326
Tauro BSuchy BCampanoni SDinda PHale KTsafrir DMUSUVATHI MGupta RAbu-Ghazaleh N(2024)TrackFM: Far-out Compiler Support for a Far Memory WorldProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3617232.3624856(401-419)Online publication date: 27-Apr-2024
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