enterprise-scale matlab applications

1© 2018 The MathWorks, Inc.

Enterprise-Scale MATLAB Applications

Sylvain Lacaze

Rory Adams

2

Preprocess DataDevelop Predictive

Models

Integrate Analytics

with Systems

MATLAB

MDCS

Request

Broker

MATLAB Production Server

Azure

IoT Hub

PI System

AWS

Kinesis

Enterprise Integration

Azure

Blob

PI System

Databases

Cloud Storage

Cosmos DB

Big Data / OT

Access and Explore

Data

Streaming

OT Platforms

Dashboards

3

MATLAB at Scale

Scale with increasing

▪ Access

▪ Computational Complexity

▪ Data Volume and Velocity

4

Key Takeaways

1. Share applications and algorithms with anyone

2. Integrate MATLAB functions into existing workflows and

development platforms.

3. Deploy MATLAB applications within enterprise systems in a

scalable manner

5

Write Once Then Share To Different Targets

MATLAB

C/C++

ExcelAdd-in Java

Hadoop

.NET

MATLAB

Compiler

MATLABProduction

ServerStandaloneApplication

MATLAB

Compiler SDK

Apps Files

Custom Toolbox

Python

WebApps

MATLAB

CoderGPU

Coder

6

Easily share apps with your team using Web Apps

▪ Use apps in browser

▪ Easy deployment

▪ No required knowledge of web

technologies

7

1

2

Integrate MATLAB-based Components With Your Own

Software

MATLAB

Toolboxes

MATLAB

Runtime

Application Author

Software Developer

43

MATLABProduction

ServerC/C++

Java

.NET

Python

MATLAB Compiler SDK

• Royalty-free Sharing

• IP Protection via Encryption

8

MATLAB

MATLAB

Compiler SDK

Customer examples: Financial customer advisory service


Request

BrokerAlgorithm

Developers

Request

Broker

Request

Broker

o Saved € 2 million

annually for an

external system

o Quicker

implementation of

adjustments in source

code by the

quantitative analysts

o Knowledge + MATLAB

= Build your own

systems

Global

financial

institution with

European HQ

9

Scaling up: Asset Allocation Demo

MATLAB Production Server(s)

HTML

XML

Java Script

Web Server(s)

10

MATLAB Production Server with Visualization Platforms

MATLAB analytics for use with desktop,

browser, and mobile visualization dashboards

▪ TableauAccess models published on MATLAB Production

Server inside Tableau calculated fields

▪ SpotfireAccess models published on MATLAB Production

Server inside Spotfire workbooks


MATLABAnalytics

TABLEAU INTERFACE

TABLEAU SERVER

11

Example: Travelling Salesman Problem with Tableau

12

MATLAB and MATLAB Production Server

▪ The easiest and most productive environment to take your enterprise

analytics solution from idea to a scalable production solution

Idea Production

13

Development

Enterprise

Application

Developer

Production

MATLAB

Developer

Production Deployment Workflow



Deployable

Archive

Web

Application

...

Function Call

MATLAB

Algorithm

MATLAB

Compiler SDK

Initial Test

Application

Debug Algorithm

Verify data

handling and

initial behavior

Web

ApplicationDeployable

Archives

Function Calls

14

Scale Up with MATLAB Production Server™

▪ Scalable and reliable– Service large numbers of concurrent requests

– Add capacity or redundancy with additional

servers

▪ Directly deploy MATLAB programs

into production– Automatically deploy updates without server

restarts

– Most efficient path for creating enterprise

applications

MATLAB Production

Server(s)

HTML

XML

Java ScriptWeb

Server(s)

15

MATLAB Production ServerEnterprise Class Framework For Running Packaged MATLAB Programs

▪ Server software

– Manages packaged MATLAB

programs and worker pool

▪ MATLAB Runtime libraries

– Single server can use runtimes from

different releases

▪ RESTful JSON interface and

lightweight client library

– Isolates the MATLAB processing

– Access using native data types


MATLAB

Runtime

Request Broker

&

Program

ManagerEnterprise

ApplicationRESTful

JSON

Enterprise

Application

MPS Client

Library

16

MATLAB at Scale


▪ Access


▪ Data Volumes

17

Key Takeaways

1. Leverage parallel computing

2. Seamlessly scale to clusters

or the cloud

18

“… can complete urgent change requests

ourselves with MATLAB, often on the same

day…Testing time has also been reduced, …

load data 8 times faster than we could do

before.”

Julian Zenglein

Commerzbank

Commerzbank

Compute a variety of derived market data

from raw market data

Aberdeen Asset Management

Improve asset allocation strategies with

machine learning techniques

“… can develop prototypes to test machine learning

techniques quickly… get rapid, reliable results by

running the algorithms with large financial data sets on

a distributed computing cluster.”

Emilio Llorente-Cano

Aberdeen Asset Management

Commerzbank headquarters in

Frankfurt.

19

Accelerating MATLAB Applications

Parallel-enabled toolboxes

Simple programming constructs

Advanced programming constructs

Ease o

f U

se

Gre

ate

r Co

ntro

l

20

Parallel-enabled Toolboxes (MATLAB® Product Family)Enable acceleration by setting a flag or preference

Optimization

Estimation of gradients

Statistics and Machine Learning

Resampling Methods, k-Means

clustering, GPU-enabled functions

Neural Networks

Deep Learning, Neural Network

training and simulation

Image Processing

Batch Image Processor, Block

Processing, GPU-enabled functions

Computer VisionBag-of-words workflow

Signal Processing and Communications GPU-enabled FFT filtering, cross

correlation, BER simulations

Other Parallel-enabled Toolboxes

http://www.mathworks.com/products/parallel-computing/parallel-support.html

21

Independent Tasks or IterationsSimple programming constructs: parfor, parfeval

▪ Examples: parameter sweeps, Monte Carlo simulations

▪ No dependencies or communications between tasks

MATLAB client

MATLAB workers

Time Time

22

Parallel ComputingMulticore Desktops

Core 3

Core 1 Core 2

Core 4

Worker Worker

Worker Worker

MATLAB multicore

http://www.mathworks.com/discovery/matlab-multicore.html

23

Parallel Computing – Scaling UpClusters/Cloud

Worker Worker Worker Worker Worker Worker


WorkerWorkerWorkerWorkerWorkerWorker


Parallel Computing Toolbox MATLAB Distributed Computing Server

24

Summary - Scale your applications beyond the desktop

Learn More: Parallel Computing on the Cloud

Option Parallel Computing Toolbox

Description Explicit desktop scaling

Maximum

workersNo limit

Hardware Desktop

Availability Worldwide

MATLAB Distributed

Computing Server

for Amazon EC2

Scale to EC2 with some

customization

256

Amazon EC2

United States, Canada and other

select countries in Europe

MATLAB Distributed

Computing Server

for Custom Cloud

Scale to custom cloud

No limit

Amazon EC2,

Microsoft Azure,Others

Worldwide

MATLAB Distributed

Computing Server

Scale to clusters

No limit

Any

Worldwide

MATLAB Distributed

Computing Server

for Hadoop + Spark

Scale to custom cloud

No limit

Hadoop + Spark

Worldwide

http://www.mathworks.com/products/parallel-computing/parallel-computing-on-the-cloud/index.html

25

MATLAB at Scale


▪ Access


▪ Data Volume and Velocity

26

▪ Batch Processing applies computation to a finite sized historical data set

that was acquired in the past

▪ Stream Processing applies computation to an unbounded data set that is

produced continuously

Messaging Service

• Reporting

• Real Time

Decision Support

Dashboards

Alerts

Storage

Historical Data

Storage

Files

Configure Resources Schedule and Run Job Output Data

Storage

Files

• Reporting

• Data Exploration

• Training Models

Connected

Devices

Continuous Data

f(x)

Stream Analytics

Big Data – Batch vs Stream Processing

27

Large Data Options

Data fits in memory of pool

▪ Distributed arrays

– Look like normal MATLAB

variables

Data does not fit in memory (Big Data)

▪ Tall arrays

▪ Looks like normal MATLAB variables

▪ Custom map-reduce functions

▪ Can be painful to learn

28

Typical Workflow – Big Data

Integrate Analytics

with Systems

Access and

Explore Data

Develop Predictive

ModelsPreprocess Data

1 2 3 4

12

23

“Package” MATLAB code &

run as automated batch

Data

MATLAB Developer

HDFS

Spark or

MapReduce

job

http://hadoop.apache.org/


29

tall arrays

▪ Data doesn’t fit into memory

▪ Lots of observations - “tall”

▪ Looks like a normal MATLAB array

– Numeric types, tables, datetimes, strings, etc…

– Basic math, stats, indexing, etc.

– Statistics and Machine Learning Toolbox

(clustering, classification, etc.)

tall array

30

Example: Taxi Data

31

▪ Tall arraysMATLAB

▪ 100’s of functions supportedMATLABStatistics and Machine Learning Toolbox

▪ Run in parallelParallel Computing Toolbox

Using Tall Arrays

Local diskShared folders

DatabasesHDFS

▪ Run in parallel on Spark clustersMATLAB Distributed Computing Server

▪ Deploy MATLAB applications as standalone applications on Spark clustersMATLAB Compiler

Spark + Hadoop

▪ Run in parallel on compute clustersMATLAB Distributed Computing Server

32

Stream Processing: Fleet Analytics

MATLAB

Analytics Development


MATLAB

Analytics

Business Decisions

MATLAB

Compiler

SDK

Algorithm

Developers

Storage

Layer

End Users

API

Gateway

AWS

Lambda

Kafka

Connector

Business

Systems

Edge

Devices

Production System

33

▪ Kafka client for MATLAB Production Server

feeds topics to functions deployed on the server

▪ Configurable batch of messages passed as a

MATLAB Timetable

▪ Each consumer process feeds one topic to a

specified function

▪ Drive everything from a simple config file

– No programming outside of MATLAB!MATLAB Production Server

Request

Broker

&

Program

Manager

Consumer

Process feeds

Topic-1

Async Java

Client

Topic-0

Partition

Partition

Partition

Topic-1

Partition

Partition

Partition

Kafka Cluster

Publisher Publisher Publisher

Consumer

Process feeds

Topic-0

Async Java

Client

Connecting MATLAB Production Server to Kafka

34

Develop a Stream Processing Function in MATLAB

35

Event

Time

Vehicle RPM Torque Fuel

Flow

… … … … …

… … … … …

… … … … …

… … … … …

… … … … …

… … … … …

… … … … …

… … … … …

… … … … …

… … … … …

… … … … …

MATLAB

Function

State

State

18:01:10 55a3fd 1975 100 110

18:10:30 55a3fe 2000 109 115

18:05:20 55a3fd 1980 105 105

18:10:45 55a3fd 2100 110 100

18:30:10 55a419 2000 100 110

18:35:20 55a419 1960 103 105

18:20:40 55a3fe 1970 112 104

18:39:30 55a419 2100 105 110

18:30:00 55a3fe 1980 110 113

18:30:50 55a3fe 2000 100 110

MATLAB

Function

State

MATLAB

Function

State

Input Table

Time window Vehicle Score

… … …

18:00:00 18:10:00 55a3fd …

55a3fe …

55a419 …

18:10:00 18:20:00 55a3fd …

55a3fe …

55a419 …

18:20:00 18:30:00 55a3fd …

55a3fe …

55a419 …

18:30:00 18:40:00 55a3fd …

55a3fe …

55a419 …

Output Table

5

7

3

9

4

5

8

Streaming Data: Treated as an Unbounded Timetable

36

Typical Workflow

Integrate Analytics

with Systems

Access and

Explore Data

Develop Predictive

ModelsPreprocess Data

1 2 3 4

12

23


MATLAB

Analytics

• Kafka

• Azure IoT Hub

• Other

Application Server:

• Streaming

• Ad-hoc analysis via

Spotfire, Tableau, …

• Other…

“Package” MATLAB code &

run as automated batch

Data

MATLAB Developer

HDFS

Spark or

MapReduce

job



37

Batch ProcessingStream ProcessingEdge

Processing with

Generated

Code, C/C++

Time critical decisions Big Data processing on historical data Near Real time decisions

Va

lue

of d

ata

to d

ecis

ion

ma

kin

g

Time

to

React

Historical

Reactive

Actionable

Pre

ve

nti

ve

/

Pre

dic

tive

Real-

TimeSeconds Minutes Hours Days Months

Kinesis

Event Hub

Time versus Value in decision making

38

Preprocess DataDevelop Predictive

Models

Integrate Analytics

with Systems

MATLAB

MDCS

Request

Broker


Azure

IoT Hub

PI System

AWS

Kinesis

Enterprise Integration

Azure

Blob

PI System

Databases

Cloud Storage

Cosmos DB

Big Data / OT

Access and Explore

Data

Streaming

OT Platforms

Dashboards

enterprise-scale matlab applications

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