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Optimizing inter-nest data locality
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Source International Conference on Compilers, Architecture and Synthesis for Embedded Systems archive
Proceedings of the 2002 international conference on Compilers, architecture, and synthesis for embedded systems table of contents
Grenoble, France
SESSION: Session S4.2: program transformation table of contents
Pages: 127 - 135  
Year of Publication: 2002
ISBN:1-58113-575-0
Authors
M. Kandemir  Pennsylvania State University, University Park, PA
I. Kadayif  Pennsylvania State University, University Park, PA
A. Choudhary  Northwestern University, Evanston, IL
J. A. Zambreno  Northwestern University, Evanston, IL
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

By examining data reuse patterns of four array-intensive embedded applications, we found that these codes exhibit a significant amount of inter-nest reuse (i. e., the data reuse that occurs between different nests). While traditional compiler techniques that target array-intensive applications can exploit intra-nest data reuse, there has not been much success in the past in taking advantage of internest data reuse. In this paper, we present a compiler strategy that optimizes inter-nest reuse using loop (iteration space) transformations. Our approach captures the impact of execution of a nest on cache contents using an abstraction called footprint vector. Then, it transforms a given nest such that the new (transformed) access pattern reuses the data left in cache by the previous nest in the code. In optimizing inter-nest locality, our approach also tries to achieve good intra-nest locality. Our simulation results indicate large performance improvements. In particular, inter-nest loop optimization generates competitive results with intra-nest loop and data optimizations.


REFERENCES

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S. P. Amarasinghe, J. M. Anderson, M. S. Lam, and C. W. Tseng. The SUIF compiler for scalable parallel machines. In Proc. the Seventh SIAM Conf. on Parallel Proc. for Scientific Computing, February, 1995.
 
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Collaborative Colleagues:
M. Kandemir: colleagues
I. Kadayif: colleagues
A. Choudhary: colleagues
J. A. Zambreno: colleagues

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