Optimizing General Compiler
Optimization
M. Haneda,
P.M.W. Knijnenburg,
and H.A.G. Wijshoff
Problem: Optimizing optimizations
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A compiler usually has many optimization settings
(e.g. peephole, delayed-branch, etc)
 gcc 3.3 has 54 optimization options
 gcc 4 has over 100 possible settings
Very little is known about how these options affect
each other
Compiler writers typically include switches that
bundle together many optimization options
 gcc –O1, -O2, -O3
…but can we do better?
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It is possible to perform better than these
predefined optimization settings, but doing so
requires extensive knowledge of the code as well
as the available optimization options
How do we define one set of options that would
work well with a large variety of programs?
Motivation for this paper
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Since there are too many optimization settings, an
exhaustive search would cost too much
 gcc 3: 2^50 different combinations!
We want to define a systematic method to find the
optimal settings, with a reduced search space
Ideally, we would like to do this with minimal
knowledge of what the options actually will do
Big Idea
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We want to find the biggest subsets of compiler
options that positively interact with each other
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Once we obtain these subsets, we will try to
combine them together, under the condition that
they do not negatively affect each other
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We will select our ultimate optimal compiler setting
from the result of these set combinations
Full vs. Fractional Factorial Design
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Full Factorial Design: explores the entire search
space, with every possible combination
 Given k options, this will take O(2^k) time
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Fractional Factorial Design: explores a reduced
search space, that is representative of the full
search space
 This can be done using orthogonal arrays
Orthogonal Arrays
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An Orthogonal Array is a matrix of 0’s and 1’s.
 The rows represent the experiments to be
performed.
 The columns represent the factors that the
experiment tries to analyze
 Any option is equally likely to be turned on/off.
 Given a particular experiment with a particular
option turned on, all the other options are still
equally likely to be turned on/off
Orthogonal Arrays
Algorithm – Step 1
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Finding maximum subsets of positively interacting
options
 Step 1.1: Find a set of options that give the best
overall improvement
 For any single optimization setting i, compute
the average speedup for all the settings in the
search space in which i is turned on
 Select M of the highest average improvement
settings
Step 1.1: Selecting the initial set
Algorithm – Step 1(cont.)
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Step 1.2: Iteratively add new options to the already
obtained sets, to get a maximum set of positively
reinforcing optimizations
 Ex: If using options A and B together produces a
more optimal setting than just using A, then add
B
 If using {A, B} and C together produces a more
optimal setting than {A, B}, then add C to {A, B}
Algorithm – Step 2
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Take the sets that we already have and try to
combine them together, assuming that they do not
negatively influence each other.
This is done to maximize the number of settings
turned on for each set
Example:
 If {A, B, C} and {D, E} do not counteract each
other, then we can combine them into {A, B, C,
D, E}
 Otherwise, leave them separate
Algorithm – Step 3
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Take the resulting sets from step 2, and select the
one with the best overall improvement.
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The result would be the ideal combination of
optimization settings, according to this
methodology.
Final Result
Comparing with existing compiler
switches
Comparing results
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The compiler setting obtained by this methodology
outpeforms –O1, -O2, and –O3 on almost all the
SPECint95 benchmarks
 -O3 performs better on li (39.2% vs. 38.4%)
 The new setting delivers the best performance
for perl (18.4% vs. 10.5%)
Conclusion
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The paper introduced a systematic way of
combining compiler optimization settings
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Used a reduced search space, constructed as
an orthogonal array
Can be done with no knowledge of actual
options
Can be done independently of architecture
Can be applied to a wide variety of applications
Future work
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Using the same methodology to find a good
optimization setting for a particular domain of
applications
Applying the methodology to newer versions of the
gcc compiler, such as gcc 4.0.1