1© 2014 The MathWorks, Inc.

Accelerating Simulink Optimization, Code

Generation & Test Automation Through

Parallelization

Ryan Chladny

Application Engineering

May 13th, 2014

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Design Challenge: Electric Vehicle

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EV Battery Topology Optimization

Independently minimize 0-60 mph time and maximize range

Combined 0-60 mph & US06 drive cycle: ~ 20min simulation

Genetic algorithm “rules of thumb”: ~70 days to converge

Is this problem feasible?

N_cells

N_strings

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75 80 85 90 9511

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13

14

15

16

17

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SOC [%]

0 t

o 6

0 m

ph

Tim

e [

s]

20

25

30

35

40

45

Multi-Objective 0-60mph + US06 Results

Cells: 424, 106s4p

Drive Ratio: 8.01:1

Ba

tte

ry R

atio

[%

]

Cells: 214, 107s2p

Drive Ratio: 6.90:1

Parallelization reduced the simulation time from 70 days to 2 !!!

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Motivation for Simulink Parallelization

Processor frequencies are flat

Number of cores are increasing

Parallelization is essential

Simulink can be parallelized

– Control tuning, design and optimization

– Code generation

– Test automation

M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, C. Batten

Extrapolations by C. Moore in:

http://www.lanl.gov/orgs/hpc/salishan/salishan2011/3moore.pdf

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EV Example: What is needed to solve the problem?

Drive cycle(s)

– Combined 0-60mph + US06

Model

– Operator, control and plant

– Variables, objectives & constraints

Algorithm

– Genetic algorithm & cost functions

Command scripts

– Initialize model & hardware

– Update dependent variables

– Collect, post-process & plot results

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EV Example: What is needed to parallelize?

Prototype on laptop and remote

desktop

Deploy to ad-hoc cluster

– 16 quad-core workstations

– 80 generations, populations of 64

Serial computation: ~70 days

Parallel computation: ~2 days

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Parallel Computing Toolbox

MATLAB Distributed Computing Server

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Parallel Computing Toolbox

MATLAB

Desktop (Client)

Local

Desktop Computer

Workers

Full MATLAB

Enables explicit use of

multi-core processors

Provides high-level tools

and infrastructure for:– creation and monitoring of jobs

– allocating hardware resources

Provides a bridge to

cluster hardware

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Parallel Computing Toolbox

MATLAB

Desktop (Client)

Local

Desktop Computer

Workers

Full MATLAB

Enables explicit use of

multi-core processors

Provides high-level tools

and infrastructure for:– creation and monitoring of jobs

– allocating hardware resources

Provides a bridge to

cluster hardware

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Simulink, Blocksets,

and Other Toolboxes

Local

Desktop Computer

MATLAB

……

MATLAB Distributed Computing Server

Cluster

Scheduler

Computer Cluster

…

…

…

…

…

…

… … …

Profile

(Local)Profile

(Cluster)

1. Prototype local parallel

code

2. Access an enabled

cluster

3. Switch local parallel

profile to cluster profile

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Code Generation

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M. Goto, “Innovation of automotive software development, Denso Corporation Keynote Address”, International Software Product Line Conference, 2013

http://splc2013.net/SPLC2013-keynote-Goto.pdf

Code Generation & Parallel Computing

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Code Generation & Parallel Computing Example

Industrial control software

– Reference models > 10

– Stateflow charts > 50

– Block count > 70,000

Intel i7-3520M, 2 (real) cores,

2.9GHz, 16Gb RAM, 5400rpm

Serial build time: 763s

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Code Generation & Parallel Computing Example

Industrial control software

– Reference models > 10

– Stateflow charts > 50

– Block count > 70,000

Intel i7-3520M, 2 (real) cores,

2.9GHz, 16Gb RAM, 5400rpm

Serial build time: 763s

Parallel build time: 438s

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Test Automation

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Test Automation & Parallel Computing Example

Fault tolerant fuel rate controller

Simulink Design Verifier generated test

harness & vectors

Serial execution: 457s

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Test Automation & Parallel Computing Example

Fault tolerant fuel rate controller

Simulink Design Verifier generated test

harness & vectors

Serial execution: 457s

Minor script adjustments

Parallel execution: 252s, ~2x faster

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Summary

System and software complexity are increasing

MathWorks parallel solutions enable full processing power of

multicore computers

Simulink can be parallelized for:

– design exploration, calibration & optimization

– code generation

– test automation

Increased throughput is equivalent to opportunity

20© 2014 The MathWorks, Inc.