genetic programming on general purpose graphics processing units (gpgpgpu) muhammad iqbal...

Genetic Programming on General Purpose Graphics Processing Units

(GPGPGPU)

Muhammad IqbalEvolutionary Computation Research Group

School of Engineering and Computer Sciences

Overview

• Graphics Processing Units (GPUs) are no longer limited to be used only for Graphics:• High degree of programmability

• Fast floating point operations

• GPUs are now GPGPUs

• Genetic programming is a computationally intensive methodology so a prime candidate for using GPUs.

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Outline

• Genetic Programming• Genetic Programming Resource Demands• GPU Programming• Genetic Programming on GPU• Automatically Defined Functions

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Genetic Programming (GP)• Evolutionary algorithm-based methodology• To optimize a population of computer programs • Tree based representation• Example:

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X Output

0 1

1 3

2 7

3 13

GP Resource Demands• GP is notoriously resource consuming

• CPU cycles

• Memory

• Standard GP system, 1μs per node• Binary trees, depth 17: 131 ms per tree

• Fitness cases: 1,000 Population size: 1,000

• Generations: 1,000 Number of runs: 100» Runtime: 10 Gs ≈ 317 years

• Standard GP system, 1ns per node» Runtime: 116 days

• Limits to what we can approach with GP

5[Banzhaf and Harding – GECCO 2009]

Sources of Speed-up

• Fast machines• Vector Processors• Parallel Machines (MIMD/SIMD)• Clusters• Loose Networks• Multi-core• Graphics Processing Units (GPU)

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Why GPU is faster than CPU ?

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The GPU Devotes More Transistors to Data Processing.

[CUDA C Programming Guide Version 3.2 ]

GPU Programming APIs

• There are a number of toolkits available for programming GPUs.

• CUDA

• MS Accelerator

• RapidMind

• Shader programming

• So far, researchers in GP have not converged on one platform

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CUDA ProgrammingMassive number (>10000) of light-weight threads.

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CUDA Memory Model

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(Device) Grid

ConstantMemory

TextureMemory

GlobalMemory

Block (0, 0)

Shared Memory

LocalMemory

Thread (0, 0)

Registers

LocalMemory

Thread (1, 0)

Registers

Block (1, 0)

Shared Memory

LocalMemory

Thread (0, 0)

Registers

LocalMemory

Thread (1, 0)

Registers

Host

CUDA exposes all the different types of memory on the GPU:

[CUDA C Programming Guide Version 3.2 ]

CUDA Programming Model

GPU is viewed as a computing device operating as a coprocessor to the main CPU (host).

• Data-parallel, computationally intensive functions should be off-loaded to the device.

• Functions that are executed many times, but independently on different data, are prime candidates, i.e. body of for-loops.

• A function compiled for the device is called a kernel.

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Stop Thinking About What to Do and Start Doing It!

• Memory transfer time expensive.

• Computation is cheap.

• No longer calculate and store in memory• Just recalculates

• Built-in variables• threadIdx

• blockIdx

• gridDim

• blockDim

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Example: Increment Array Elements

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Example: Matrix Addition

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Example: Matrix Addition

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Parallel Genetic Programming

While most GP work is conducted on sequential

computers, the following computationally intensive

features make it well suited to parallel hardware:

• Individuals are run on multiple independent training examples.

• The fitness of each individual could be calculated on independent hardware in parallel.

• Multiple independent runs of the GP are needed for statistical confidence to the stochastic element of the result.

18[Langdon and Banzhaf, EuroGP-2008]

A Many Threaded CUDA Interpreter for Genetic Programming

• Running Tree GP on GPU

• 8692 times faster than PC without GPU

• Solved 20-bits Multiplexor• 220 = 1048576 fitness cases

• Has never been solved by tree GP before

• Previously estimated time: more than 4 years

• GPU has consistently done it in less than an hour

• Solved 37-bits Multiplexor• 237 = 137438953472 fitness cases

• Has never been attempted before

• GPU solves it in under a day

19[W.B.Langdon, EuroGP-2010]

Boolean Multiplexor

d = 2a

n = a + dNum test cases = 2n

20-mux 1 million test cases37-mux 137 billion test cases

20[W.B.Langdon, EuroGP-2010]

Genetic Programming Parameters for Solving 20 and 37 Multiplexors

Terminals 20 or 37 Boolean inputs D0 – D19 or D0 – D36 respectively

Functions AND, OR, NAND, NOR

Fitness Pseudo random sample of 2048 of 1048576 or 8192 of 137438953472 fitness cases.

Tournament 4 members run on same random sample. New samples for each tournament and each generation.

Population 262144

Initial Population

Ramped half-and-half 4:5 (20-Mux) or 5:7 (37-Mux)

Parameters 50% subtree crossover, 5% subtree 45% point mutation. Max depth 15, max size 511 (20-Mux) or 1023 (37-Mux)

Termination 5000 generations

21[W.B.Langdon, EuroGP-2010]

Solutions are found in generations 423 (20-Mux) and 2866 (37-Mux).

AND, OR, NAND, NOR

X Y X & Y

0 0 0

0 1 0

1 0 0

1 1 1

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X Y X d Y

0 0 1

0 1 1

1 0 1

1 1 0

X Y X r Y

0 0 1

0 1 0

1 0 0

1 1 0

X Y X | Y

0 0 0

0 1 1

1 0 1

1 1 1

AND: &

NOR: rNAND: d

OR: |

Evolution of 20-Mux and 37-Mux

23[W.B.Langdon, EuroGP-2010]

6-Mux Tree I

24[W.B.Langdon, EuroGP-2010]

6-Mux Tree II

25[W.B.Langdon, EuroGP-2010]

6-Mux Tree III

26[W.B.Langdon, EuroGP-2010]

Ideal 6-Mux Tree

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Automatically Defined Functions (ADFs)

• Genetic programming trees often have repeated patterns.

• Repeated subtrees can be treated as subroutines.

• ADFs is a methodology to automatically select and implement modularity in GP.

• This modularity can:• Reduce the size of GP tree

• Improve readability

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Langdon’s CUDA Interpreter with ADFs

• ADFs slow down the speed• 20-Mux taking 9 hours instead of less than an hour

• 37-Mux taking more than 3 days instead of less than a day

• Improved ADFs Implementation• Previously used one thread per GP program

• Now using one thread block per GP program• Increased level of parallelism

• Reduced divergence

• 20-Mux taking 8 to 15 minutes

• 37-Mux taking 7 to 10 hours

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6-Mux with ADF

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6-Mux with ADF

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6-Mux with ADF

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Conclusion 1: GP

• Powerful machine learning algorithm

• Capable of searching through trillions of states to find the solution

• Often have repeated patterns and can be compacted by ADFs

• But computationally expensive

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Conclusion 2: GPU• Computationally fast

• Relative low cost

• Need new programming paradigm, which is practical.

• Accelerates processing speed up to 3000 times for computationally intensive problems.

• But not well suited for memory intensive problems.

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Acknowledgement

• Dr Will Browne and Dr Mengjie Zhang for Supervision.

• Kevin Buckley for Technical Support.

• Eric for helping in CUDA compilation.

• Victoria University of Wellington for Awarding “Victoria PhD Scholarship”.

• All of You for Coming.

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Thank You

Questions?