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Predictor virtualization
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Architectural Support for Programming Languages and Operating Systems archive
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems table of contents
Seattle, WA, USA
SESSION: Microarchitecture table of contents
Pages 157-167  
Year of Publication: 2008
ISBN:978-1-59593-958-6
Also published in ...
Authors
Ioana Burcea  University of Toronto, Toronto, ON, Canada
Stephen Somogyi  Carnegie-Mellon University, Pittsburgh, PA
Andreas Moshovos  University of Toronto, Toronto, ON, Canada
Babak Falsafi  Carnegie-Mellon University, Pittsburgh, PA
Sponsors
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGPLAN: ACM Special Interest Group on Programming Languages
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 11,   Downloads (12 Months): 179,   Citation Count: 3
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APPENDICES and SUPPLEMENTS
Supplemental material for Predictor virtualization


ABSTRACT

Many hardware optimizations rely on collecting information about program behavior at runtime. This information is stored in lookup tables. To be accurate and effective, these optimizations usually require large dedicated on-chip tables. Although technology advances offer an increased amount of on-chip resources, these resources are allocated to increase the size of on-chip conventional cache hierarchies.

This work proposes Predictor Virtualization, a technique that uses the existing memory hierarchy to emulate large predictor tables. We demonstrate the benefits of this technique by virtualizing a state-of-the-art data prefetcher. Full-system, cycle-accurate simulations demonstrate that the virtualized prefetcher preserves the performance benefits of the original design, while reducing the on-chip storage dedicated to the predictor table from 60KB down to less than one kilobyte.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Ferdman, M., and Falsafi, B. Last-Touch Correlated Data Streaming. In Proc. of the Intl' Symposium on Performance Analysis of Systems and Software, 2007.
 
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Jerger, N., Hill, E., and Lipasti, M. Friendly Fire: Understanding the Effects of Multiprocessor Prefetching. In Proc. of the International Symposium on Performance Analysis of Systems and Software, 2006.
 
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Tendler, J., Dodson, S., and Fields, S. IBM eServer Power4 System Microarchitecture, Technical White Paper, IBM Server Group, 2001
 
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VMWare -- http://www.vmware.com
 
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Collaborative Colleagues:
Ioana Burcea: colleagues
Stephen Somogyi: colleagues
Andreas Moshovos: colleagues
Babak Falsafi: colleagues