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CIM: A Reliable Metric for Evaluating Program Phase Classifications
January-June 2007 (vol. 6 no. 1)
pp. 9-12
We propose the use of the Confidence Interval of estimated Mean (CIM), a metric based on statistical sampling theory, to evaluate the quality of a given phase classification and for comparing different phase classification schemes. Previous research on phase classification used the Weighted Average of Coefficient of Variation (CoVwa) to estimate phase classification quality. We found that the phase quality indicated by CoVwa could be inconsistent across different phase classifications. We explain the reasons behind this inconsistency and demonstrate the inconsistency using data from several SPEC CPU2000 benchmark programs. We show that the Confidence Interval of estimated Mean (CIM) correctly estimates the quality of phase classification with a meaningful statistical interpretation.
Index Terms:
Benchmark Analysis, Phase Classification, Quality Metric, Statistical Sampling
Citation:
Sreekumar Kodakara, Jinpyo Kim, David Lilja, Douglas Hawkins, Wei-Chung Hsu, Pen-Chung Yew, "CIM: A Reliable Metric for Evaluating Program Phase Classifications," IEEE Computer Architecture Letters, vol. 6, no. 1, pp. 9-12, Jan.-June 2007, doi:10.1109/L-CA.2007.4
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