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RapidMRC: approximating L2 miss rate curves on commodity systems for online optimizations
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Architectural Support for Programming Languages and Operating Systems archive
Proceeding of the 14th international conference on Architectural support for programming languages and operating systems table of contents
Washington, DC, USA
SESSION: Prediction and accounting table of contents
Pages 121-132  
Year of Publication: 2009
ISBN:978-1-60558-406-5
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Authors
David K. Tam  University of Toronto, Toronto, Canada
Reza Azimi  University of Toronto, Toronto, Canada
Livio B. Soares  University of Toronto, Toronto, Canada
Michael Stumm  University of Toronto, Toronto, Canada
Sponsors
SIGPLAN: ACM Special Interest Group on Programming Languages
SIGOPS: ACM Special Interest Group on Operating Systems
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
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ABSTRACT

Miss rate curves (MRCs) are useful in a number of contexts. In our research, online L2 cache MRCs enable us to dynamically identify optimal cache sizes when cache-partitioning a shared-cache multicore processor. Obtaining L2 MRCs has generally been assumed to be expensive when done in software and consequently, their usage for online optimizations has been limited. To address these problems and opportunities, we have developed a low-overhead software technique to obtain L2 MRCs online on current processors, exploiting features available in their performance monitoring units so that no changes to the application source code or binaries are required. Our technique, called RapidMRC, requires a single probing period of roughly 221 million processor cycles (147 ms), and subsequently 124 million cycles (83 ms) to process the data. We demonstrate its accuracy by comparing the obtained MRCs to the actual L2 MRCs of 30 applications taken from SPECcpu2006, SPECcpu2000, and SPECjbb2000. We show that RapidMRC can be applied to sizing cache partitions, helping to achieve performance improvements of up to 27%.


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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Collaborative Colleagues:
David K. Tam: colleagues
Reza Azimi: colleagues
Livio B. Soares: colleagues
Michael Stumm: colleagues