Abstract
Recent work has showed that program analysis techniques to select
meaningful features of programs are important in predictive
modeling on compiler optimization selection problems.
Although there are many successful state-of-the-art program analysis techniques,
they often do not provide enough loop information which can
be helpful, especially when a target program is computation intensive
with many complex loops.
In this paper, we introduce a novel technique to characterize loops using a pattern-driven system named HERCULES and automatically select compiler optimizations. Using this system allows us to derive meaningful and expressive loop features which is not trivial to collect using other static techniques in state-of-the-art.
We evaluate our loop features by building prediction models and
compare the models based on three successful state-of-the-art program characterization techniques
found in the literature. We show that our models outperform
three state-of-the-art program characterization techniques introduced in this paper
in terms of buildling a prediction model.
We show that our models with loop features achieved up to
63% of the best speedup we can reach in our given optimization space for one of platforms
we conducted experiments.
In addition, we also show how prediction can be affected when we use our loop features
and one of dynamic features together.