CISC673 – Optimizing Compilers 1/34

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

• University of Delaware

Phase Ordering

CISC673 – Optimizing Compilers 2/34

Optimization??

does it really work??

_

209_

db

_

213_

javac

_

222_

mpe

gaud

io

_

202_

jess

_

201_

com

pres

s

_

205_

rayt

race

_

228_

jack

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

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 solution

Input = 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

0.94

0.960.98

11.02

1.04

1.061.08

1.1

0 200 400 600 800 1000

Generation

Sp

eed

up

s

Max Fitness

O3 (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