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Summary: Aestimo: A Feedback-Directed Optimization Evaluation Tool
Paul Berube, Jos´e Nelson Amaral
Dept. of Computing Science, University of Alberta
Edmonton, Alberta, T6G 2E8, Canada
Abstract
Published studies that use feedback-directed optimiza-
tion (FDO) techniques use either a single input for both
training and performance evaluation, or a single input for
training and a single input for evaluation. Thus an impor-
tant question is if the FDO results published in the literature
are sensitive to the training and testing input selection.
Aestimo is a new evaluation tool that uses a workload of
inputs to evaluate the sensitivity of specific code transfor-
mations to the choice of inputs in the training and testing
phases. Aestimo uses optimization logs to isolate the effects
of individual code transformations. It incorporates metrics
to determine the effect of training input selection on indi-
vidual compiler decisions.
Besides describing the structure of Aestimo, this paper
presents a case study that uses SPEC CINT2000 benchmark
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