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Statistical Selection of Compiler Options. R.P.J. Pinkers P.M.W. Knijnenburg M. Haneda H.A.G. Wijshoff. Motivation. Modern compilers contain dozens of options. Options can positively or negatively interfere.

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statistical selection of compiler options

Statistical Selectionof Compiler Options

R.P.J. Pinkers

P.M.W. Knijnenburg

M. Haneda

H.A.G. Wijshoff

motivation
Motivation
  • Modern compilers contain dozens of options.
  • Options can positively or negatively interfere.
  • Optimal setting of those options depends on application as well as target architecture.
  • Standard –Ox settings produce sub-optimal results.
  • We propose almost automatic iterative procedure to select options for a given application and architecture based on statistical analysis
orthogonal arrays
Orthogonal Arrays
  • If there are N options or factors, the full optimization space contains 2N combinations.
  • This space is called afull factorial design.
  • A fractional factorial design is a subset of the full factorial design.
  • An Orthogonal Array (OA) or Taguchi design is a well-known approach to fractional factorial designs.
  • An OA allows us to determine the effect of a factor in the presence of other factors using a reduced space.
definition of orthogonal arrays
Definition of Orthogonal Arrays
  • OA is N x k matrix of zeroes and ones.
  • The columns are interpreted as options.
  • Each row defines a compiler setting.
  • An OA has the property that two arbitrary columns contain the patterns

equally often.

  • Each option Oi is turned on and off equally often.
  • If Oi is turned on, then each other option Oj is turned on and off equally often.

00 01 10 11

orthogonal array example
Orthogonal Array (Example)

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 1 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 1 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 1 1 1 1 0 0 1 1 0 0 0 0 1 1 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0

main effects of options
Main Effects of Options
  • An OA allows us to calculate the main effect of options.
  • The main effect of an option Oi wrt an OA A is defined as

where s denotes a row in A and T(s) denotes the execution time of the program when compiles with setting s.

relative effects
Relative Effects
  • Execution times are given in cycles or seconds.
  • Effects of options for different benchmarks cannot be compared easily.
  • We define relative effect of an option Oi as
improvement of options
Improvement of Options
  • An option can improve or degrade performance.
  • The main effect is always positive and does not distinguish between improvement or degradation.
  • We define the improvement of an option Oi wrt an OA A as
iterative algorithm
Iterative Algorithm

Repeat:

  • Compile application with each row from A as compiler setting and execute optimized application.
  • Compute effect of each option.
  • If effect is larger than threshold of 10%
    • if option has positive improvement, switch option on
    • else switch option off.
  • Construct new OA A by dropping columns corresponding to selected options.

until all options are set

experimental setup
Experimental Setup
  • SimpleScalar simulator with GCC 2.6.3.
  • Contains 19 options divided in 14 factors
  • 6 SPEC95 benchmarks.
conclusions
Conclusions
  • We have proposed a method based on statistical analysis of the effect of options using Orthogonal Arrays.
  • Method uses (almost) no knowledge of compiler.
  • Can be implemented as a simple driver on top of any compiler.
  • Significant improvement can be obtained by automatically tuning compiler options over standard optimization settings.
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