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Optimizing General Compiler Optimization

Optimizing General Compiler Optimization. M. Haneda, P.M.W. Knijnenburg, and H.A.G. Wijshoff. Problem: Optimizing optimizations. 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

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Optimizing General Compiler Optimization

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  1. Optimizing General Compiler Optimization M. Haneda, P.M.W. Knijnenburg, and H.A.G. Wijshoff

  2. Problem: Optimizing optimizations • 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

  3. …but can we do better? • 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?

  4. Motivation for this paper • 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

  5. Big Idea • We want to find the biggest subsets of compiler options that positively interact with each other • Once we obtain these subsets, we will try to combine them together, under the condition that they do not negatively affect each other • We will select our ultimate optimal compiler setting from the result of these set combinations

  6. Full vs. Fractional Factorial Design • Full Factorial Design: explores the entire search space, with every possible combination • Given k options, this will take O(2^k) time • Fractional Factorial Design: explores a reduced search space, that is representative of the full search space • This can be done using orthogonal arrays

  7. Orthogonal Arrays • 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

  8. Orthogonal Arrays

  9. Algorithm – Step 1 • 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

  10. Step 1.1: Selecting the initial set

  11. Algorithm – Step 1(cont.) • 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}

  12. Algorithm – Step 2 • 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

  13. Algorithm – Step 3 • Take the resulting sets from step 2, and select the one with the best overall improvement. • The result would be the ideal combination of optimization settings, according to this methodology.

  14. Final Result

  15. Comparing with existing compiler switches

  16. Comparing results • 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%)

  17. Conclusion • The paper introduced a systematic way of combining compiler optimization settings • 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

  18. Future work • 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

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