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Analysis of redundancy and application balance in the SPEC CPU2006 benchmark suite
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International Symposium on Computer Architecture archive
Proceedings of the 34th annual international symposium on Computer architecture table of contents
San Diego, California, USA
SESSION: Experience and methodology table of contents
Pages: 412 - 423  
Year of Publication: 2007
ISBN:978-1-59593-706-3
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Authors
Aashish Phansalkar  The University of Texas at Austin, Austin, TX
Ajay Joshi  The University of Texas at Austin, Austin, TX
Lizy K. John  The University of Texas at Austin, Austin, TX
Sponsors
SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS : Computer Society
Publisher
ACM  New York, NY, USA
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ABSTRACT

The recently released SPEC CPU2006 benchmark suite is expected to be used by computer designers and computer architecture researchers for pre-silicon early design analysis. Partial use of benchmark suites by researchers, due to simulation time constraints, compiler difficulties, or library or system call issues is likely to happen; but a random subset can lead to misleading results. This paper analyzes the SPEC CPU2006 benchmarks using performance counter based experimentation from several state of the art systems, and uses statistical techniques such as principal component analysis and clustering to draw inferences on the similarity of the benchmarks and the redundancy in the suite and arrive at meaningful subsets.

The SPEC CPU2006 benchmark suite contains several programs from areas such as artificial intelligence and includes none from the electronic design automation (EDA) application area. Hence there is a concern on the application balance in the suite. An analysis from the perspective of fundamental program characteristics shows that the included programs offer characteristics broader than the EDA programs' space. A subset of 6 integer programs and 8 floating point programs can yield most of the information from the entire suite.


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:
Aashish Phansalkar: colleagues
Ajay Joshi: colleagues
Lizy K. John: colleagues