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A component model of spatial locality
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International Symposium on Memory Management archive
Proceedings of the 2009 international symposium on Memory management table of contents
Dublin, Ireland
SESSION: Paper session 4 table of contents
Pages 99-108  
Year of Publication: 2009
ISBN:978-1-60558-347-1
Authors
Xiaoming Gu  Intel China Research Center, Beijing, China
Ian Christopher  University of Rochester, Rochester, NY, USA
Tongxin Bai  University of Rochester, Rochester, NY, USA
Chengliang Zhang  Microsoft Corporation, Redmond, WA, USA
Chen Ding  University of Rochester, Rochester, NY, USA
Sponsors
SIGPLAN: ACM Special Interest Group on Programming Languages
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Good spatial locality alleviates both the latency and bandwidth problem of memory by boosting the effect of prefetching and improving the utilization of cache. However, conventional definitions of spatial locality are inadequate for a programmer to precisely quantify the quality of a program, to identify causes of poor locality, and to estimate the potential by which spatial locality can be improved.

This paper describes a new, component-based model for spatial locality. It is based on measuring the change of reuse distances as a function of the data-block size. It divides spatial locality into components at program and behavior levels. While the base model is costly because it requires the tracking of the locality of every memory access, the overhead can be reduced by using small inputs and by extending a sampling-based tool. The paper presents the result of the analysis for a large set of benchmarks, the cost of the analysis, and the experience of a user study, in which the analysis helped to locate a data-layout problem and improve performance by 7% with a 6-line change in an application with over 2,000 lines.


REFERENCES

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K. Beyls and E. D'Hollander. Discovery of locality-improving refactoring by reuse path analysis. In Proceedings of HPCC. Springer. Lecture Notes in Computer Science Vol. 4208, pages 220--229, 2006.
 
5
6
 
7
8
9
 
10
 
11
 
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M. Hirzel and T. M. Chilimbi. Bursty tracing: A framework for low-overhead temporal profiling. In Proceedings of ACM Workshop on Feedback-Directed and Dynamic Optimization, Dallas, Texas, 2001.
 
13
14
15
 
16
G. Marin and J. Mellor-Crummey. Scalable cross-architecture predictions of memory hierarchy response for scientific applications. In Proceedings of the Symposium of the Las Alamos Computer Science Institute, Sante Fe, New Mexico, 2005.
 
17
R. L. Mattson, J. Gecsei, D. Slutz, and I. L. Traiger. Evaluation techniques for storage hierarchies. IBM System Journal, 9(2):78--117, 1970.
18
 
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21
22
23
 
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25
 
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X. Shen, Y. Zhong, and C. Ding. Regression-based multi-model prediction of data reuse signature. In Proceedings of the 4th Annual Symposium of the Las Alamos Computer Science Institute, Sante Fe, New Mexico, November 2003.
 
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Spec cpu benchmarks. http://www.spec.org/benchmarks.html\#cpu.
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Collaborative Colleagues:
Xiaoming Gu: colleagues
Ian Christopher: colleagues
Tongxin Bai: colleagues
Chengliang Zhang: colleagues
Chen Ding: colleagues