Embracing Technical Computing Trends with MATLAB Accelerating the Pace of Engineering and Science

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1 Embracing Technical Computing Trends with MATLAB Accelerating the Pace of Engineering and Science Graz, 14. Oktober 2016 Michael Glaßer Dipl.-Ing. Senior Application Engineer 2016 The MathWorks, Inc. 1

obvious: Interactive + programmatic Code analysis environment

2 Elements of a Great Technical Computing Environment Obvious components: Data analysis environment Programming environment Less obvious: Interactive + programmatic Code analysis environment Hardware connectivity Automation of workflows Scalability and Deployment Collaboration 2

3 MATLAB A Platform for Technical Computing Access Files Data Explore & Create Data Analysis Surface fitting Share Reports and Report Documentation Software Equations Hardware Equations Mathematical Modeling Algorithm Development Derivation & solving x E = V R Application Development Optimization y Application Applications Report Outputs for Design Application F = ma 3

4 MATLAB A Platform for Technical Computing Access Image Processing and Computer Vision Explore & Discover Computational Finance Share Files Data Analysis & Modeling Reporting and Documentation Data Analysis Software Algorithm Development Algorithm Development Mathematical Modeling Outputs for Design Code & Applications Hardware Digital Signal Processing Application Development Communications Deployment Control Systems Automate 4

5 Focusing on Three Types of Modeling Parametric Modeling Uses a known model that maps independent variables to dependent with a set of constant unknowns Black-box Modeling Uses an automatically created model that learns to map the independent variables to the dependent variable First Principle Modeling Uses a model derived directly from the laws of physics without making assumptions such as empirical or fitted parameters 5

6 Live Editor Accelerate exploratory programming and analysis Create interactive narratives Use live scripts to teach 6

7 Trends in Technical Computing 1. Mobile Cloud Computing 2. Rise of Data Analytics and Machine Learning 3. Advanced Algorithms for Everyone 4. Mainstreaming of Low Cost Hardware 5. Increasing Quality Demands 7

8 What are Your Challenges? or trends you re interested? referring to today s theme: Trends in algebraic and numeric computing systems 8

9 Trends in Technical Computing 1. Mobile Cloud Computing 2. Rise of Data Analytics and Machine Learning 3. Advanced Algorithms for Everyone 4. Mainstreaming of Low Cost Hardware 5. Increasing Quality Demands 9

10 What is the Cloud? Compute is not local Using applications that are not installed on your computer or mobile device Using external compute resources to power applications installed on your devices Storage is not local Storing data outside your computer or mobile device Source: 10

11 MATLAB Online 11

12 MATLAB Mobile Support for iphone, ipad & Android 12

13 13

14 Faculty Creates Course 14

15 Faculty Defines Assignments & Problems 15

16 Students Invited into Course 16

17 Students Do Coursework 17

But then I learn there's a command that does something I didn't

18 Learning Analytics After the solution I come up with ends up in the middle of the graph I feel disappointed. But then I learn there's a command that does something I didn't know about and I feel better about myself because I learned a new command! Rodolfo on Cody 18

19 Cody Coursework Catalog Instructor access only Copy pre-built problems and courses Organize and edit 20

20 Cloud Computing From Your Desktop MATLAB Distributed Computing Server on Amazon EC2 23

21 ThingSpeak Collect data from internet-connected sensors and run MATLAB analytics on the cloud using functions from: Statistics and Machine Learning Toolbox Signal Processing Toolbox Curve Fitting Toolbox Mapping Toolbox 24

22 Example: Deploying MATLAB Applications to the Cloud 25

23 Trends in Technical Computing 1. Mobile Cloud Computing 2. Rise of Data Analytics and Machine Learning 3. Advanced Algorithms for Everyone 4. Mainstreaming of Low Cost Hardware 5. Increasing Quality Demands 26

24 Data-Driven Decisions and Data-Driven Design Rapid growth of measurement devices, data, and compute power Corresponding growth of analysis Lots of data (and many variables) Lots of compute power required Systems are often too complex to know the governing equation (e.g., black-box modeling) An opportunity for better decisions and design Critical need for rapid decision making and rapid design iteration remains 27

25 Data Analytics in the Industry (Auto, Aero, Machinery, Medical Devices, etc.) Data Analytics DATA Engineering, Scientific, and Field Business and Transactional Embedded Systems Developed with Model-Based Design Enterprise IT Systems 28

26 Why MATLAB? MATLAB Analytics work with business and engineering data 1 DATA Engineering, Scientific, and Field Business and Transactional Data Analytics MATLAB lets engineers do Data Science themselves 2 MATLAB Analytics run anywhere 3 Embedded Systems Developed with Model-Based Design Enterprise IT Systems 29

27 Machine Learning 30

28 Machine Learning is Everywhere Image Recognition Speech Recognition Stock Prediction Medical Diagnosis Data Analytics Robotics and more [TBD] 31

29 Machine Learning Machine learning uses data and produces a program to perform a task Task: Human Activity Detection Standard Approach Machine Learning Approach Computer Program Machine Learning Hand Written Program If X_acc > 0.5 then SITTING If Y_acc < 4 and Z_acc > 5 then STANDING Formula or Equation Y activity = β 1 X acc + β 2 Y acc + β 3 Z acc + model: Inputs Outputs model = < Machine Learning >(sensor_data, activity) Algorithm 32

30 essentially, all models are wrong, but some are useful George Box 33

31 Different Types of Learning Type of Learning Categories of Algorithms Supervised Learning Classification Output is a choice between classes (True, False) (Red, Blue, Green) Machine Learning Develop predictive model based on both input and output data Regression Output is a real number (temperature, stock prices) Unsupervised Learning Clustering No output - find natural groups and patterns from input data only Discover an internal representation from input data only 34

32 Different Types of Learning Type of Learning Categories of Algorithms Supervised Learning Classification Support Vector Machines Discriminant Analysis Naive Bayes Nearest Neighbor Machine Learning Develop predictive model based on both input and output data Regression Linear Regression GLM SVR, GPR Ensemble Methods Decision Trees Neural Networks Unsupervised Learning Clustering kmeans, kmedoids Fuzzy C-Means Hierarchical Gaussian Mixture Discover an internal representation from input data only Neural Networks Hidden Markov Model 35

33 Machine Learning Workflow Train: Iterate till you find the best model LOAD DATA PREPROCESS DATA SUPERVISED LEARNING MODEL FILTERS PCA CLASSIFICATION SUMMARY STATISTICS CLUSTER ANALYSIS REGRESSION Predict: Integrate trained models into applications NEW DATA PREDICTION 36

34 Example: Classification Machine Learning Type of Learning Supervised Learning Develop predictive model based on both input and output data Categories of Algorithms Classification Regression Objective: Train a classifier to classify human activity from sensor data Data: Inputs 3-axial Accelerometer 3-axial Gyroscope Outputs Unsupervised Learning Clustering Approach: Import data Discover an internal representation from input data only Interactively train and compare classifiers Test results on new sensor data 37

Machine Learning Workflow for Example Train: Iterate till you find the best model LOAD DATA PREPROCESS DATA SUPERVISED LEARNING MODEL FILTERS SUMMARY STATISTICS PCA CLUSTER ANALYSIS

35 Machine Learning Workflow for Example Train: Iterate till you find the best model LOAD DATA PREPROCESS DATA SUPERVISED LEARNING MODEL FILTERS SUMMARY STATISTICS PCA CLUSTER ANALYSIS CLASSIFICATION Classification Learner REGRESSION Predict: Integrate trained models into applications TEST DATA PREPROCESS DATA MODEL PREDICTION FILTERS PCA SUMMARY STATISTICS CLUSTER ANALYSIS 38

Classification Learner App to apply advanced classification methods to your data Added to Statistics and Machine Learning Toolbox in R2015a Adding in

36 Classification Learner App to apply advanced classification methods to your data Added to Statistics and Machine Learning Toolbox in R2015a Adding in R2015b: Discriminant analysis Dimension reduction via PCA Parallel coordinates plot Categorical predictors Also: Table and categorical support via command line 40

37 Example: Regression Type of Learning Categories of Algorithms Objective: Supervised Learning Classification Easy and accurate computation of day-ahead system load forecast Machine Learning Develop predictive model based on both input and output data Regression Unsupervised Learning Clustering Discover an internal representation from input data only 41

Example: Clustering Machine Learning Type of Learning Supervised Learning Develop predictive model based on both input and output data Categories of Algorithms Classification

38 Example: Clustering Machine Learning Type of Learning Supervised Learning Develop predictive model based on both input and output data Categories of Algorithms Classification Regression Objective: Given data for engine speed and vehicle speed, identify clusters Unsupervised Learning Clustering Discover an internal representation from input data only 42

39 What is Deep Learning? Deep learning performs end-end learning by learning features, representations and tasks directly from images, text and sound Traditional Machine Learning Manual Feature Extraction Classification Machine Learning Car Truck Bicycle Deep Learning approach Convolutional Neural Network (CNN) Learned features 95% End-to-end learning 3% Feature learning + Classification 2% Car Truck Bicycle 44

40 Demo : Live Object Recognition with Webcam 45

41 Trends in Technical Computing 1. Mobile Cloud Computing 2. Rise of Data Analytics 3. Advanced Algorithms for Everyone 4. Mainstreaming of Low Cost Hardware 5. Increasing Quality Demands 46

42 Making New Technologies Accessible From x = A\b to Apps New technologies, techniques, and data sources are becoming available at a rapid rate e.g. GPUs, big data, cloud computing, mobile computing, apps You d like to take advantage of these, but They re complex They require new algorithms Do you have the time/background to invest heavily in all of them? We make it accessible Learn and apply (immediately) 47

43 High Performance Computing Multicore and Multiprocessor Support Threaded libraries for multicore math operations, since R2007b. Support for additional functions in each release 48

44 Parallel Computing Paradigm Multicore Desktops Multicore Desktop MATLAB Desktop (client) Core 1 Core 2 Worker Worker Core 5 Worker Core 6 Worker MATLAB multicore 49

Parallel Computing Paradigm Cluster Hardware Cluster of computers Core 1 Core 2 Core 1 Core 2 Worker Worker Worker Worker Core 5 Core 6 Core 5 Core 6 Worker

45 Parallel Computing Paradigm Cluster Hardware Cluster of computers Core 1 Core 2 Core 1 Core 2 Worker Worker Worker Worker Core 5 Core 6 Core 5 Core 6 Worker Worker Worker Worker MATLAB Desktop (client) Core 1 Core 2 Core 1 Core 2 Worker Worker Worker Worker Core 5 Core 6 Core 5 Core 6 Worker Worker Worker Worker 50

46 High Performance Computing Multicore and Multiprocessor Support Regular MATLAB for loop for i = 1:10 end long_running_code parfor in Parallel Computing Toolbox parpool parfor i = 1:10 long_running_code end 51

truss Cross sectional area of truss elements Mx ሷ + Cx ሶ + Kx = F Convert

47 Example: Parameter Sweep Parallel for-loops Deflection of customizable truss Initial dynamic load, damping Parameters investigated: Height of truss Cross sectional area of truss elements Mx ሷ + Cx ሶ + Kx = F Convert for to parfor Use pool of MATLAB workers Scale the same algorithm to run on a cluster 52

48 Benchmark: Parameter Sweep of ODEs Scaling case study with a compute cluster Workers in pool values Compute time (minutes) 144 values 16 values Processor: Intel Xeon E5-class v2 16 physical cores per node MATLAB R2014a 53

49 Parallel Computing Paradigm NVIDIA GPUs Using NVIDIA GPUs MATLAB Desktop (client) GPU cores Device Memory 54

Time (seconds) Run Same Code on CPU and GPU Solving 2D Wave Equation 80 70

Grid size CPU Intel(R) Xeon(R) W3550 3.06GHz 4 cores memory bandwidth 25.

50 Time (seconds) Run Same Code on CPU and GPU Solving 2D Wave Equation x faster 23x faster 20x faster Grid size CPU Intel(R) Xeon(R) W GHz 4 cores memory bandwidth 25.6 Gb/s GPU NVIDIA Tesla K20c 706MHz 2496 cores memory bandwith 208 Gb/s 55

51 High Performance Computing GPU Example Required Code Changes 56

52 Run Same Code on GPU or CPU: overloaded functions 57

53 Interactive Algorithms with MATLAB Apps 58

54 Finding More Apps 59

55 Add-On Explorer Add capabilities to MATLAB, including community-authored and MathWorks toolboxes, apps, functions, models, and hardware support Browse and install add-ons directly from MATLAB Access community-authored content from File Exchange 60

56 How can you make your algorithms more accessible? 61

App Designer Integrates the Primary Tasks of App

performing common tasks App Designer makes app building

visual design and code development App Designer

57 App Designer Integrates the Primary Tasks of App Building Apps are interactive applications for performing common tasks App Designer makes app building more efficient by letting you quickly move between visual design and code development App Designer includes: Enhanced design environment Expanded UI component set Code integration 62

58 Authoring Apps 63

59 Authoring Apps Filter Design 64

60 Authoring Apps 65

61 Trends in Technical Computing 1. Mobile Cloud Computing 2. Rise of Data Analytics 3. Advanced Algorithms for Everyone 4. Mainstreaming of Low Cost Hardware 5. Increasing Quality Demands 66

2560), $55 (Due) Raspberry Pi 10,000,000th Pi shipped in 2016 $35

62 Mainstreaming of Low Cost Hardware Arduino 1.000,000+ have been commercially produced $30 (UNO), $55 (Mega 2560), $55 (Due) Raspberry Pi 10,000,000th Pi shipped in 2016 $35 (Model B) LEGO Mindstorms EV3 3 rd generation from LEGO Mindstorms $350 (for base set) 67

63 Low Cost Hardware Tethered Approach 69

64 Low Cost Hardware Embedded Approach 70

65 Download Hardware Support Packages 71

66 72

67 Trends in Technical Computing 1. Mobile Cloud Computing 2. Rise of Data Analytics 3. Advanced Algorithms for Everyone 4. Mainstreaming of Low Cost Hardware 5. Increasing Quality Demands 73

68 Code Quality Writing better code Less error-prone Human readable code Performance tuning Robustness Validate, guard inputs/outputs Handle errors, exceptions 74

69 Improving Code Robustness in MATLAB Design Decisions Checking McCabe complexity Input and error handling oncleanup for clean exit Style guidelines 75

70 Input and Error Handling Check input arguments Validateattributes isscalar, isnumeric, varargin, nargin inputparser Handle exceptions try catch MException Warn or Error assert warning, error 76

71 Effectively Test Your Code in MATLAB MATLAB Unit Test Framework Script-based interface Function-based interface Object-oriented interface Report generation and publishing 77

GIT and Subversion integration in Current Folder browser Use

72 Source Control Integration Manage your code from within the MATLAB Desktop Leverage modern source control capabilities GIT and Subversion integration in Current Folder browser Use Comparison Tool to view and merge changes between revisions 78

73 Collaborating using GitHub 79

74 Trends in Technical Computing 1. Mobile Cloud Computing 2. Rise of Data Analytics and Machine Learning 3. Advanced Algorithms for Everyone 4. Mainstreaming of Low Cost Hardware 5. Increasing Quality Demands 80

75 2016 The MathWorks, Inc. 81

Data Analytics with MATLAB Adam Filion Application Engineer MathWorks

Data Analytics with MATLAB Adam Filion Application Engineer MathWorks Data Analytics with Adam Filion Application Engineer MathWorks 2015 The MathWorks, Inc. 1 Case Study: Day-Ahead Load Forecasting Goal: Implement a tool for easy and accurate computation of dayahead system