CISC673 – Optimizing Compilers 1/34

• Presented by: Sameer Kulkarni• Dept of Computer & Information Sciences

• University of Delaware

Phase Ordering

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Optimization??

does it really work??

_2

09_d

b

_2

13_ja

vac

_2

22_m

pega

udio

_2

02_je

ss

_2

01_c

ompre

ss

_2

05_ra

ytrac

e

_2

28_ja

ck0

0.20.40.60.8

11.21.41.61.8

2

SpecJVM98 relative speedup

O1O2O3

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No. of optimizations

• O64 = 264 (on last count)• JikesRVM = 67

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Search space

• Consider a hypothetical case where we apply 40 optimizations

• O64 : 3.98 x 1047 • Jikes: 4.1 x 1018

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Could take a while

• Considering the smaller problem, assume that running all the benchmarks take just 1 sec to run

• Jikes would take: 130.2 billion years • Age of the universe 13 billion years

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Some basic Optimizations

• Constant Sub-expression Elimination• Loop Unrolling• Local Copy Prop • Branch Optimizations ...

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Example

for(int i=0; i< 3;i++){

a = a + i + 1;

}

Loop Unrolling

CSE

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Instruction Scheduling vs Register Allocation

• Maximizing Parallelism IS• Minimizing Register Spilling RA

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Phase Ord. vs Opt Levels

• Opt Levels ~ Timing Constraints• Phase ordering ~ code interactions

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Whimsical??

• Opt X would like to go before Opt Y, but not always.

Best d

b

Best ja

vac

Best m

pega

udio

Best je

ss

Best r

aytra

ce

Best C

ompr

ess

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

_209_db _213_javac _222_mpegaudio _202_jess _205_raytrace _201_compress

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Ideal Solution?

• Oracle Perfect sequence at the very start• Wise Man Solution Given the present code

predict the best optimization solution

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Wise Man

?• Understand

Compilers

• Optimizations

• Source Code

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Possible Solutions• Pruning the search space• Genetic Algorithms• Estimating running times• Precompiled choices

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Pruning Search space

Fast and Efficient Searches for Effective Optimization Phase Sequences, Kulkarni et al. TACO 2005

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Optimization Profiling

Fast and Efficient Searches for Effective Optimization Phase Sequences, Kulkarni et al. TACO 2005

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Genetic Algorithms

Fast Searches for Effective Optimization Phase Sequences, Kulkarni et al. PLDI ‘04

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Exhaustive vs Heuristic [2]

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Disadvantages• Benchmark Specific• Architecture dependent• Code disregarded

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Improvements• Profiling the application• Understand the code• Understanding optimizations• Continuous evaluation of transformations

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Proposed solutionInput = Code Features

Output = Running time

Evolve Neural Networks

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Proposed solution

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Experimental Setup• Neural Network Evolver (ANJI)• Training Set { javaGrande }• Testing Set { SpecJVM, Da Capo }

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ANJI• Mutating & generating n/w s• Network phase ordering• Timing Information• Scoring the n/w

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Training Phase• Generations and Chromosomes• Random chromosomes• Back Propagation• Add/Remove/Update hidden nodes

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Experimental Setup

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Network Evolution

Anji Evolution over generations

0.940.960.98

11.021.041.061.08

1.1

0 200 400 600 800 1000

Generation

Spee

dups

Max FitnessO3 (rough)

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Network Evolution

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javaGrande• Set of very small benchmarks• Low running times• Memory management• Machine Architecture

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Testing• SpecJVM’98 & Da Capo• Champion n/w• Running times

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Present Solution

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Implementation in GCC• Milepost GCC

• Created for intelligent compilation

• Collecting source features

• Submitting features to common loc.

• Hooks into the Compilation process.

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Possible Use Case

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Structure for Phase Ordering

ANJI network from Source features

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LLVM• Open Source Compiler• Modular Design• Easy to work with• All Optimizations are interchangeable

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Questions

Most of the files and this presentation have been uploaded to http://www.cis.udel.edu/~skulkarn/ta.html