This document discusses techniques for working with large datasets in MATLAB. It recommends using sparse matrices, categorical arrays, and vectorization to reduce memory usage. It also suggests breaking large data into pieces and using block or stream processing. Distributed computing allows offloading work to clusters for faster processing and more memory. Benchmarking shows significant speedups using Amazon EC2 cluster instances compared to a desktop.

1. 1© 2014 The MathWorks, Inc.
Modellbildung, Berechnung und Simulation
in Forschung und Lehre
Dr. Joachim Schlosser
MathWorks
Simulation im Computer Aided Engineering,
HFT-Stuttgart, 4.7.2014
With material from Dr. Mischa Kim, Dr. Rainer Muemmler

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“There is no correlation between
experimenting and learning outcome, in general.” §
“Experimenting does improve learning outcome,
if supported by a didactic framework.” §
Not if, but how…
§ e.g. D. R. Sokoloff and R. K. Thornton, Using Interactive Lecture Demonstrations to Create an
Active Learning Environment. The Physics Teacher, September, 1997, Vol. 35, pp. 340-347.

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“Experiments…
• Activate knowledge, thereby
• Support the learning process
• Foster competences and
• Motivate students” §
The Why’s
§ e.g. M Hopf, H Schecker, H Wiesner, Physik allgemein / Physkdidaktik kompakt.

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About
Learningand
Teaching

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Experiments are time consuming.
1. Prep work (instructor)
2. Pre-experiment
3. Experiment
4. Post-experiment
5. Evaluation
The How’s
MATLAB & Simulink Very important
Immensely important
Typically over-emphasized

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Hardware connectivity
Symbolic Numeric
Optimization
Control System
Graphic, physical
Modeling
Simscape
StateFlow
Simulink
MATLAB
Simulink and Simscape blocks
MATLAB function
Publishing
Symbolic
Experiment implementation: the sky’s the limit
1
1
1
1
2
2
2
2

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The solution landscape: a matter of perspective
MATLAB (symbolic)
Simulink
Simscape
MATLAB (numeric)
Publishing
MATLAB and H/W
convert
convert
convert

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RLC series circuit: problem statement

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RLC series circuit: solution and analysis

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Resources to get you started
>> doc
MATLAB
Courseware
MATLAB Central
info/code hub

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Online experimenting: Cody Coursework Problem
Solution
Feedback
 Create MATLAB problem sets
 Grade based on test suites (automated!)
 Assess class progress
 Detect plagiarism and proctor exams
 Use 1300+ autograde-ready MATLAB
programming problems

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Hardware for experiments
Support package only req’d Additional toolboxes req’d
Raspberry Pi
LEGO Mindstorms
NXT and EV3
Arduino
Samsung Galaxy, iPhone
Altera DE2-
115
Freescale Freedom
Digilent’s
Analog
Discovery
 Find hardware for your application
…plus many more…plus many more
Microsoft
Kinect
NI
CompactDAQ

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 Retain the right students, on-time: STEOP
 Integrate Spiraling Curricula
Standardized Tool in Curricula

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Key takeaways
 Experiments can foster the learning progress.
Use with care.
 Experiments develop competences.
 MathWorks provides tools and resources for your support.

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Challenge
Teach mathematics and mathematical modeling to
students across multiple disciplines and two universities,
and prepare them to meet the needs of industry
Solution
Integrate MATLAB throughout the core mathematics
curriculum and provide interdisciplinary exercises in
practical problem-solving
Results
 Interdepartmental collaboration improved
 Students taught skills required by industry
 Students self-identified as expert users
Chalmers University of Technology
Integrates MATLAB Throughout Core
Mathematics Curriculum
Chalmers University in Gothenburg, Sweden.
Link to user story
“We train our students to use
MATLAB as an interactive tool not
only for completing the assigned
exercises but also for investigating
mathematical problems and having
fun. We are increasing our focus
on interdisciplinary programs, and
MATLAB is perfect for integrating
mathematics with other subjects.”
Dr. Tommy Gustafsson
Chalmers University of Technology

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University of Applied Sciences Augsburg
Students Develop and Simulate Advanced
Robotic Control Systems
Challenge
Enable students to participate in the development of
advanced robotic control software
Solution
Integrate MathWorks tools into exercises in modeling
robotic systems and implementing control software
Results
 Programming skills quickly acquired
 Reusable robotic control components developed
 Students’ transition to industry eased
“When I teach C++, I show students a
program that simulates a swing. The C++
program is seven pages of code or more.
The MATLAB implementation is a single
page —about 50 lines of well-structured,
compact code that is easy to
understand.”
Professor Georg Stark
University of Applied Sciences Augsburg
Link to user story
Professor Stark with students in the lab.

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Technische Universität München Uses
Model-Based Design to Drive Research,
Problem-Based Learning, and Industry
Collaboration
Challenge
Enable problem-based learning of flight dynamics
and cost-effective implementation of flight control
systems
Solution
Use MathWorks tools for Model-Based Design to
simulate designs, conduct real-time tests, and
develop realistic flight simulators
Results
 Students prepared for a variety of careers
 Motivation increased and learning accelerated
 Collaboration with industry partners strengthened
“Flight controls and flight system dynamics are
multidomain engineering disciplines. MathWorks
tools enable our students to build upon our
fundamental research to develop solutions that fly
in real aircraft. With Model-Based Design we can
close the gap between the theoretical foundation
and the practical application, and that is how we
measure success.”
Dr. Florian Holzapfel
Technische Universität München
Link to user story
Professor Holzapfel, research fellow Markus Hornauer,
and a student test flight control algorithms in the
Research Flight Simulator.

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And what about
Specialties in
Research?

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Festo Develops Innovative Robotic Arm
Using Model-Based Design
Challenge
Design and implement a control system for a pneumatic
robotic arm
Solution
Use Simulink and Simulink PLC Coder to model, simulate,
optimize, and implement the controller on a programmable
logic controller
Results
 Complex PLC implementation automated
 Technology and innovation award won
 New business opportunities opened
“Using Simulink for Model-Based Design enables us to
develop the sophisticated pneumatic controls required
for the Bionic Handling Assistant and other
mechatronic designs. With Simulink PLC Coder, it is
now much easier to get from a design to a product.”
Dr. Rüdiger Neumann
Festo
Link to user story
The Festo Bionic Handling Assistant.
Image © Festo AG.

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All of the previous applies.

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But you process more data.
Much more data.

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Max Planck Institute Reconstructs Key
Protein Complexes Using MATLAB and
Parallel Computing Toolbox
Challenge
Develop high-quality 3D images of protein complexes
Solution
Use MathWorks tools to acquire, analyze, filter, combine,
and display electron microscope images
Results
 Research time cut by years
 Development time cut from weeks to days
 Workflow accelerated
“Parallel Computing Toolbox enabled us
to speed up our processing by 20 to 30
times. We were able to use our cluster
productively from the MATLAB
environment without having to be experts
in parallel programming or having to learn
another programming language.”
Andreas Korinek
Max Planck Institute of Biochemistry
Schematic of the 26S proteasome.
Link to user story

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Challenges with Large Data
 “Out of memory”
 Slow processing
– Data too large to be efficiently managed between RAM and virtual
memory
– Lots of data
 Gaining insight
– Large data visualization
– Modeling with no equation and lots of predictors

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Data Copies
Function calls
 Data is “copy-on-write” (lazy-copy)
 Passed by reference into the function
function y = foo(x,a,b)
y = a * x + b;
end
function y = foo(x,a,b)
a(1) = a(1) + 12;
y = a * x + b;
end
a not copied
a is copied
Is not modified, no copy is made Is modified, a temporary copy is made

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Data Copies
In-Place Optimizations
 MATLAB perform calculations “in-place” when:
– Output variable name is the same as input variable name
– Performing element-wise computation
not in-place
y = 2*x + 3;
x = 2*x + 3;
in-place

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Sparse Matrices
 Require less memory and are faster
 When to use sparse?
< 1/2 dense on 64-bit (double precision)
< 2/3 dense on 32-bit (double precision)
 Functions that support sparse matrices
>> help sparfun
 Blog Post: Creating Sparse Finite Element Matrices
blogs.mathworks.com/loren/2007/03/01/creating-sparse-finite-element-matrices-in-matlab/

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Categorical Arrays
Ordinal and Nominal Arrays
 Memory-efficient data container for values
drawn from a finite, discrete set of categories
 Nominal arrays
– No ordering in the levels
 Ordinal arrays
– Ordering in the levels
Available from
 Statistics Toolbox

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Vectorization and Memory Tradeoffs
 Use bsxfun to limit creating intermediate matrices
– Applies element-wise binary operation with singleton expansion
 Consider using several smaller matrices for processing
– Keep within the limits of contiguous memory
– Process with for loops (i.e. de-vectorize)
– Slower runtime but will use less memory
– Operate on data by column for best performance
N x M+,*N x 1
1 x M
=
Element-wise
operation

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Processing Large Data Sets
 Break your large data into separate pieces
and process independently
– Partial reading and writing of files
– Built-in functionality for block-processing
– System Objects for stream processing
 Use the whole dataset at once
– Single array across memory of multiple machines

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Reading in Part of a Dataset from Files
 ASCII file
– Import Tool , textscan
 MAT file
– Load and save part of a variable using the matfile
 Binary file
– Read and write directly to/from file using memmapfile
– Maps address space to file
 Databases
– ODBC and JDBC-compliant (e.g. Oracle, MySQL, Microsoft, SQL Server)
– Database Explorer App

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Block Processing Images
 blockproc automatically divides an
image into blocks for processing
 Reduces memory usage
– Read and write block directly from image file
 Processes arbitrarily large images
Available from
 Image Processing Toolbox

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Batch processing…
Load the entire file and process it all at once
Stream processing
Load a frame and process it before moving on to the next frame
Source
Batch
Processing
Algorithm
Memory
MATLAB Memory
Stream
Source
Stream
Processing

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System Objects
 A class of MATLAB objects that support streaming workflows
 Simplifies data access for streaming applications
 Contain algorithms to work with streaming data
Available from
 DSP System Toolbox
 Communications System Toolbox
 Computer Vision System Toolbox
 Phased Array System Toolbox
 Image Acquisition Toolbox

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Distributed Array
Lives on the Workers
Remotely Manipulate Array
from Client
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Distributing Large Data
Worker
Worker
Worker
Worker
MATLAB
Desktop (Client)
Available from
 Parallel Computing Toolbox
 MATLAB Distributed Computing Server

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 Offload computation:
– Free up desktop
– Access better computers
 Scale speed-up:
– Use more cores
– Go from hours to minutes
 Scale memory:
– Utilize distributed arrays
– Solve larger problems without re-coding algorithms
Cluster
Computer Cluster
Scheduler
Take Advantage of Cluster Hardware
MATLAB
Desktop (Client)

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 On bw|HPC-C5
 Installed on Grid Nodes KIT, Freiburg, Tübingen
 Supported by MathWorks Pilot Team: install, documentation, usage
 Also on bwGRiD
 Installed on Grid Nodes Ulm/Konstanz, Mannheim, Tübingen

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Benchmarking Ab on EC2
 Using Amazon EC2's
cluster compute instances
 A up to 290 GB
 Good desktop machine
 4-20 GB of RAM
 20-60 Gigaflops
 Up to 1.3 Teraflops,
over a 20X improvement

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Technical Resources
 MATLAB documentation User’s Guide
Programming Fundamentals  Software Development  Memory Usage
 The Art of MATLAB, Loren Shure’s blog: blogs.mathworks.com/loren/
 Memory Management Guides
mathworks.com/support/tech-notes/1100/1106.html
mathworks.com/support/tech-notes/1100/1107.html
 MATLAB Answers: mathworks.com/matlabcentral/answers/

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