Copyright by William Lloyd Bircher 2010 - The Laboratory for ...

3. Based on basic domain knowledge, choose performance events, measureable by
performance counters that are most relevant to the subsystem in question. Choose
counters that are expected to “trickle-down” to other subsystems. The pool of candidate
performance counters may need to be expanded if sufficient accuracy is not achieved.
4. Using the selected performance counter events as the input variables and
subsystem power as the output variable, perform linear regression modeling. For
example, in the general linear equation y = mx + b, vary the coefficients m and b until the
sum-of-squares error is minimized. Multiple linear or polynomial regression may be
used in subsequent iterations of algorithm if sufficient accuracy is not obtained.
5. Using a subset of workloads calculate average error per sample. If less than ρ%
error cannot be achieved, choose an a new performance event. Selection of ρ is dictated
by the required model accuracy and time required for solution. Setting ρ to a low
(restrictive) value may extend time to solution. It may also prevent the process from
finding a solution.
6. Assess the representativeness of the model by manually comparing graphs of
modeled versus measured power. This avoids the case in which statistical assessment
cannot detect major errors such as those seen in Anscombe’s Quartet [An73].
7. Using complete set of workloads calculate average error per sample. If less than δ
% error cannot be achieved, choose a new performance event. Like ρ, δ is selected
according the accuracy and time-to-solution requirements.
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