Getting Started with MATLAB Francesca Perino
|
|
- Earl Goodman
- 6 years ago
- Views:
Transcription
1
2 Getting Started with MATLAB Francesca Perino 2014 The MathWorks, Inc. 1
3 Agenda MATLAB Intro Importazione ed esportazione Programmazione in MATLAB Tecniche per la velocizzazione dell esecuzione del codice e gestione di grosse basi dati Parallel Computing Generazione di codice C 2
4 Connecting to data from/to different sources Files Research and Quantify Data Analysis & Visualization Share Reporting Financial Modeling Data repositories Datafeeds Application Development Applications 3
5 Interaction with Excel How to import/export data from/to Excel - MATLAB -Can I perform that in an easy way? xlsread xlswrite -Can I have some control or customize the interaction? actxserver 4
6 Interface boundary Excel-MATLAB interaction over COM MATLAB supports COM integration on Windows, where COM stands for Microsoft Component Object Model 5
7 MATLAB Data Type Numeric Types Integer and floating-point data Characters and Strings Text in character arrays Structures Arrays with named fields that can contain data of varying types and sizes Cell Arrays Arrays that can contain data of varying types and sizes 6
8 MATLAB Data Type Tables Arrays in tabular form whose named columns can have different types Categorical Arrays Arrays of qualitative data with values from a finite set of discrete, nonnumeric data Datetime Arrays of date and time values that can be displayed in different formats 7
9 Ways to work with Image Files 8
10 Playing with Images 9
11 Steps Loading and showing of images Analysis in grayscale (histogram analysis) Binary Image Morphological operation in binary, and grayscale Distance, and watershed transform Property analysis 10
12 Acceleration Strategies 1. Best practices in programming Identify bottlenecks (e.g. Profiler, Code analyzer) Vectorization & pre-allocation Technology / Product 2. Better algorithms Different algorithmic approach to solve the same problem 3. More processors, cores, and GPUs Utilize high level parallel constructs (e.g. parfor) Scale to clusters, grids, and clouds 4. Re-implement to move to another language Generate & compiled code (MEX, C, HDL) Run on FPGAs or DSP 11
13 Speed up your MATLAB Image Processing Application We can speed up some tasks by taking advantage of high-performance computing resources, such as a multicore computers and GPU. Writing your task in parallel, you can: Run an application across a range of high-performance computing resources Program and execute parallel code interactively or in batch mode 12
14 Case study: a photo mosaic application A photographic mosaic is an image that is created from many smaller images. The effect is to recreate a picture by replacing its small portions with another image (tile) that has the same average color. At a distance, the mosaic will look like the original picture, while up close, the individual tiles can be seen. It is not difficult to make a photomosaic in MATLAB. 13
15 Case study: a photo mosaic application We need 1. to extract the average RGB value for each tile image 2. to split the image into sub-images that are the same size as your tile-set and then iterates through each one. 3. For each sub-image, the function computes the average and then the distance between that average and the pre-computed averages of all the tiles 4. The distances for each sub-sub-image are summed for that sub-image, and the tile in the tile-set with the minimum cumulative distance is inserted in the sub-image. 14
16 sub-image elaboration Case study: a photo mosaic application Block1 Block2 Block3 15
17 Ease of Use Programming Parallel Applications (CPU) Built-in support with toolboxes Simple programming constructs: parfor, batch, distributed Advanced programming constructs: createjob, labsend, spmd Greater Control 16
18 Image Block Processing The blockproc function in Image Processing Toolbox lets you work with really big images by processing them efficiently a block at a time. Computations run in parallel on multiple cores and GPUs when used with Parallel Computing Toolbox. 17
19 Graphics Processing Units (GPUs) Originally for graphics acceleration, now also used for scientific calculations Massively parallel array of integer and floating point processors Typically hundreds of processors per card GPU cores complement CPU cores Dedicated high-speed memory 18
20 General Purpose GPU Areas Signal processing Image and video processing Computational chemistry Bioinformatics and medical imaging Computational finance Energy production 19
21 Common Terms Used in GPU Computing CUDA : A parallel computing technology from NVIDIA Consists of a parallel computing architecture and developer tools, libraries, and programming directives for GPU computing Device: Card containing GPU and associated memory Host: CPU and system memory Kernel: Code written for execution on the GPU Functions that can run on a large number of threads Parallelism from each thread independently running the same program on different data 20
22 Criteria for Good Problems to Run on a GPU Massively parallel: Able to break down calculations into hundreds or thousands of independent units of work Motivation: Best performance when hundreds of GPU cores are kept busy Computationally intensive: Computation time should significantly exceed time spent on data transfer to and from GPU Motivation: Data transfer is costly since GPU is attached to CPU via the PCI Express bus 21
23 GPU Support with Parallel Computing Toolbox NVIDIA GPUs with compute capability 1.3 or greater Includes Tesla 10-series and 20-series products (e.g., NVIDIA Tesla C2075 GPU: 448 processors, 6 GB memory) Why we require compute capability 1.3 Support doubles (base data type in MATLAB) Guarantee IEEE compliance Provide cross-platform support 22
24 Ease of Use Options for Targeting GPUs Use GPU array interface with MATLAB built-in functions Execute custom functions on elements of the GPU array Create kernels from existing CUDA code and PTX files Greater Control 23
25 Overloaded MATLAB Functions A = magic(1000); G = gpuarray(a); %Push to GPU memory b = rand(1000,1,'gpuarray'); %Create on GPU F = fft(g); x = G\b; z = gather(x); %Bring back into MATLAB Full list of built-in functions that support GPUArray User s Guide GPU Computing Using GPUArray 24
26 Ease of Use Options for Targeting GPUs Use GPU array interface with MATLAB built-in functions Execute custom functions on elements of the GPU array Create kernels from existing CUDA code and PTX files Greater Control 25
27 Using arrayfun on GPU gain = 1.5; offset = -0.1; x = rand(1000,1, 'gpuarray'); %Create on GPU fh mygpufun(x, gain, offset); x = arrayfun(fh, x)%execute on GPU function c = mygpufun(x, gain, offset) c = (x.* gain) + offset; end Full list of functions for use with arrayfun on GPU User s Guide GPU Computing Execute MATLAB Code on a GPU 26
28 Ease of Use Options for Targeting GPUs Use GPU array interface with MATLAB built-in functions Execute custom functions on elements of the GPU array Create kernels from existing CUDA code and PTX files Greater Control 27
29 Invoking CUDA Kernels % Setup kernel = parallel.gpu.cudakernel( mykern.ptx, mykern.cu ); % Configure kernel.threadblocksize = 512; kernel.gridsize = [2 2]; % Run [c, d] = feval(kernel, a, b); 28
30 Best Practices for using GPU with MATLAB Profile your code to identify your bottlenecks Work on large enough matrices to see the benefits of GPU parallelization Minimize data transfer between CPU and GPU Sustained use of supported functionality Create variables directly on the GPU Use array indexing and branching in moderation Combine multiple element-wise calculations together into a single function call by using arrayfun 29
31 Automatic Translation of MATLAB to C Maintain one design in MATLAB Design faster and get to C/C++ quickly Test more systematically and frequently Spend more time improving algorithms in MATLAB iterate Algorithm Design and Code Generation in MATLAB verify / accelerate 30
32 Using MATLAB Coder: 3-Step Workflow Prepare your MATLAB algorithm Make implementation choices Prepare Use supported language features Test if your MATLAB code is compliant Validate that MATLAB program generates code (MEX) Test Accelerate execution of user-written algorithm Generate Generate source code or MEX for final use Iterate your MATLAB code to optimize Implement as source, executable or library C/C++ MEX 31
33 The role of MATLAB Coder MATLAB algorithm C/C++ project MATLAB Coder 32
34 Big Data Strategies Work with data too large to fit into system memory Access data from multiple sources including SQL databases, data historians, instrumentation, and files Visualize and interact with data 33
35 Large Data Analytics on the Desktop Prototype Access Explore Share/Deploy Scale Access big data from your desktop Collections of Text Files Databases Binary Files datastore Database Toolbox memmapfile 34
36 Large Data Analytics on the Desktop Expand workspace 64 bit processor support increased in-memory data set handling Access portions of data too big to fit into memory Memory mapped variables huge binary file Datastore huge text file or collections of text files Database query portion of a big database table Variety of programming constructs System Objects analyze streaming data MapReduce process text files that won t fit into memory 35
37 Scaled Large Data Analytics Prototype Access Explore Share/Deploy Scale Load, Analyze, Discard datastore, parfor MapReduce Distributed Memory SPMD out-ofmemory in-memory Embarrassingl y Parallel Complexity Non- Partitionable 36
38 Reading in Multiple Files Files with equivalent formats and well ordered file names can be read in using a for-loop. Speed advantages can be gained by using parfor. parfor l = 1:no_files fid = fopen([ data',num2str(l),'.txt']); ww = textscan(fid,'%f %f'); fclose(fid); end time(:,l) = ww{:,1}; data(:,l) = ww{:,2}; In-memory Big Data Analysis with PCT and MDCS 37
39 Streaming Algorithms 38
40 Streaming Algorithms For developing efficient, readable stream processing programs in MATLAB, System objects: process frames and then overwrite past frames with incoming data initialize parameters only once as they are created manage buffer updates, state updates, and indexing automatically, which speeds algorithm development support MATLAB code generation and parallel computing workflows 39
41 Support for Communications System Toolbox GPU implementations of LDPC Decoder, Viterbi Decoder, AWGN Channel, PSK Modulator, Block Interleaver, Block Deinterleaver DVB-S System Simulation Demo 40
42 MATLAB Data Storage 41
43 Distributed Arrays Using Parallel Computing Toolbox and MATLAB Distributed Computing Server, you can work with matrices and multidimensional arrays that are distributed across the memory of a cluster of computers. Using this approach, you can store and perform computations on big data sets that are too large to fit in a single computer s memory. 42
44 Strengths of MATLAB for Large Data Analytics Challenge Getting started Rapid data exploration MATLAB Solution Easy access to data from your desktop Tools for accessing typical big data sets consisting of text or binary files, contained in database tables or stored on Hadoop All the tools to explore and visualize data Easy to try different methods Ideal environment for developing your own methods Development of scalable algorithms Use within business systems Work on the desktop and scale to clusters Tools for use in analyzing big data on your desktop, which scale for use on clusters, including Hadoop, if needed Ease of deployment and leveraging enterprise Push-button deployment into production including support for Hadoop 43
Data Analytics with MATLAB. Tackling the Challenges of Big Data
Data Analytics with MATLAB Tackling the Challenges of Big Data How big is big? What characterises big data? Any collection of data sets so large and complex that it becomes difficult to process using traditional
More informationMit MATLAB auf der Überholspur Methoden zur Beschleunigung von MATLAB Anwendungen
Mit MATLAB auf der Überholspur Methoden zur Beschleunigung von MATLAB Anwendungen Frank Graeber Application Engineering MathWorks Germany 2013 The MathWorks, Inc. 1 Speed up the serial code within core
More informationSpeeding up MATLAB Applications Sean de Wolski Application Engineer
Speeding up MATLAB Applications Sean de Wolski Application Engineer 2014 The MathWorks, Inc. 1 Non-rigid Displacement Vector Fields 2 Agenda Leveraging the power of vector and matrix operations Addressing
More informationAccelerating System Simulations
Accelerating System Simulations 김용정부장 Senior Applications Engineer 2013 The MathWorks, Inc. 1 Why simulation acceleration? From algorithm exploration to system design Size and complexity of models increases
More informationParallel and Distributed Computing with MATLAB The MathWorks, Inc. 1
Parallel and Distributed Computing with MATLAB 2018 The MathWorks, Inc. 1 Practical Application of Parallel Computing Why parallel computing? Need faster insight on more complex problems with larger datasets
More informationModeling a 4G LTE System in MATLAB
Modeling a 4G LTE System in MATLAB Part 2: Simulation acceleration Houman Zarrinkoub PhD. Signal Processing Product Manager MathWorks houmanz@mathworks.com 2011 The MathWorks, Inc. 1 Why simulation acceleration?
More informationParallel and Distributed Computing with MATLAB Gerardo Hernández Manager, Application Engineer
Parallel and Distributed Computing with MATLAB Gerardo Hernández Manager, Application Engineer 2018 The MathWorks, Inc. 1 Practical Application of Parallel Computing Why parallel computing? Need faster
More informationMulticore Computer, GPU 및 Cluster 환경에서의 MATLAB Parallel Computing 기능
Multicore Computer, GPU 및 Cluster 환경에서의 MATLAB Parallel Computing 기능 성호현 MathWorks Korea 2012 The MathWorks, Inc. 1 A Question to Consider Do you want to speed up your algorithms? If so Do you have a multi-core
More informationParallel Computing with MATLAB
Parallel Computing with MATLAB CSCI 4850/5850 High-Performance Computing Spring 2018 Tae-Hyuk (Ted) Ahn Department of Computer Science Program of Bioinformatics and Computational Biology Saint Louis University
More information2015 The MathWorks, Inc. 1
2015 The MathWorks, Inc. 1 What s New in Release 2015a and 2014b Young Joon Lee Principal Application Engineer 2015 The MathWorks, Inc. 2 Agenda New Features Graphics and Data Design Performance Design
More informationOptimizing and Accelerating Your MATLAB Code
Optimizing and Accelerating Your MATLAB Code Sofia Mosesson Senior Application Engineer 2016 The MathWorks, Inc. 1 Agenda Optimizing for loops and using vector and matrix operations Indexing in different
More informationData Analytics with MATLAB
Data Analytics with MATLAB Tackling the Challenges of Big Data Adrienne James, PhD MathWorks 7 th October 2014 2014 The MathWorks, Inc. 1 Big Data in Industry ENERGY Asset Optimization FINANCE Market Risk,
More informationWorking with Large Sets of Images in MATLAB Just Got Easier Avi Nehemiah
Working with Large Sets of Images in MATLAB Just Got Easier Avi Nehemiah 2015 The MathWorks, Inc. 1 Challenges Posed by Large Sets of Images 1. How do I import several thousand images into MATLAB? 2. Can
More informationTechnical Computing with MATLAB
Technical Computing with MATLAB University Of Bath Seminar th 19 th November 2010 Adrienne James (Application Engineering) 1 Agenda Introduction to MATLAB Importing, visualising and analysing data from
More informationMit MATLAB auf der Überholspur Methoden zur Beschleunigung von MATLAB Anwendungen
Mit MATLAB auf der Überholspur Methoden zur Beschleunigung von MATLAB Anwendungen Michael Glaßer Application Engineering MathWorks Germany 2014 The MathWorks, Inc. 1 Key Takeaways 1. Speed up your serial
More informationIntegrate MATLAB Analytics into Enterprise Applications
Integrate Analytics into Enterprise Applications Lyamine Hedjazi 2015 The MathWorks, Inc. 1 Data Analytics Workflow Preprocessing Data Business Systems Build Algorithms Smart Connected Systems Take Decisions
More informationLarge Data in MATLAB: A Seismic Data Processing Case Study U. M. Sundar Senior Application Engineer
Large Data in MATLAB: A Seismic Data Processing Case Study U. M. Sundar Senior Application Engineer 2013 MathWorks, Inc. 1 Problem Statement: Scaling Up Seismic Analysis Challenge: Developing a seismic
More informationIntegrate MATLAB Analytics into Enterprise Applications
Integrate Analytics into Enterprise Applications Aurélie Urbain MathWorks Consulting Services 2015 The MathWorks, Inc. 1 Data Analytics Workflow Data Acquisition Data Analytics Analytics Integration Business
More informationMATLAB. Senior Application Engineer The MathWorks Korea The MathWorks, Inc. 2
1 Senior Application Engineer The MathWorks Korea 2017 The MathWorks, Inc. 2 Data Analytics Workflow Business Systems Smart Connected Systems Data Acquisition Engineering, Scientific, and Field Business
More informationHigh Performance and GPU Computing in MATLAB
High Performance and GPU Computing in MATLAB Jan Houška houska@humusoft.cz http://www.humusoft.cz 1 About HUMUSOFT Company: Humusoft s.r.o. Founded: 1990 Number of employees: 18 Location: Praha 8, Pobřežní
More information개발과정에서의 MATLAB 과 C 의연동 ( 영상처리분야 )
개발과정에서의 MATLAB 과 C 의연동 ( 영상처리분야 ) Application Engineer Caleb Kim 2016 The MathWorks, Inc. 1 Algorithm Development with MATLAB for C/C++ Programmers Objectives Use MATLAB throughout algorithm development
More informationParallel Computing with MATLAB on Discovery Cluster
Parallel Computing with MATLAB on Discovery Cluster Northeastern University Research Computing: Nilay K Roy, MS Computer Science, Ph.D Computational Physics Lets look at the Discovery Cluster Matlab environment
More informationBIG DATA: Data Analytics with MATLAB Christophe POUILLOT Senior Consultant MathWorks
BIG DATA: Data Analytics with MATLAB Christophe POUILLOT Senior Consultant MathWorks christophe.pouillot@mathworks.fr 2014 The MathWorks, Inc. 1 Definition of Big Data Data so large and complex that it
More informationMoving MATLAB Algorithms into Complete Designs with Fixed-Point Simulation and Code Generation
Moving MATLAB Algorithms into Complete Designs with Fixed-Point Simulation and Code Generation Houman Zarrinkoub, PhD. Product Manager Signal Processing Toolboxes The MathWorks Inc. 2007 The MathWorks,
More informationGPU ACCELERATED DATABASE MANAGEMENT SYSTEMS
CIS 601 - Graduate Seminar Presentation 1 GPU ACCELERATED DATABASE MANAGEMENT SYSTEMS PRESENTED BY HARINATH AMASA CSU ID: 2697292 What we will talk about.. Current problems GPU What are GPU Databases GPU
More informationScaling MATLAB. for Your Organisation and Beyond. Rory Adams The MathWorks, Inc. 1
Scaling MATLAB for Your Organisation and Beyond Rory Adams 2015 The MathWorks, Inc. 1 MATLAB at Scale Front-end scaling Scale with increasing access requests Back-end scaling Scale with increasing computational
More information2^48 - keine Angst vor großen Datensätzen in MATLAB
2^48 - keine Angst vor großen Datensätzen in MATLAB 9. July 2014 Rainer Mümmler Application Engineering Group 2014 The MathWorks, Inc. 1 Challenges with Large Data Sets Out of memory Running out of address
More informationWhat s New MATLAB and Simulink
What s New MATLAB and Simulink Ascension Vizinho-Coutry Application Engineer Manager MathWorks Ascension.Vizinho-Coutry@mathworks.fr Daniel Martins Application Engineer MathWorks Daniel.Martins@mathworks.fr
More informationIntroduction to C and HDL Code Generation from MATLAB
Introduction to C and HDL Code Generation from MATLAB 이웅재차장 Senior Application Engineer 2012 The MathWorks, Inc. 1 Algorithm Development Process Requirements Research & Design Explore and discover Design
More informationModeling a 4G LTE System in MATLAB
Modeling a 4G LTE System in MATLAB Part 3: Path to implementation (C and HDL) Houman Zarrinkoub PhD. Signal Processing Product Manager MathWorks houmanz@mathworks.com 2011 The MathWorks, Inc. 1 LTE Downlink
More informationSpeeding up MATLAB Applications The MathWorks, Inc.
Speeding up MATLAB Applications 2009 The MathWorks, Inc. Agenda Leveraging the power of vector & matrix operations Addressing bottlenecks Utilizing additional processing power Summary 2 Example: Block
More informationParallel Computing with MATLAB
Parallel Computing with MATLAB Jos Martin Principal Architect, Parallel Computing Tools jos.martin@mathworks.co.uk 1 2013 The MathWorks, Inc. www.matlabexpo.com Code used in this presentation can be found
More informationAdvanced Software Development with MATLAB
Advanced Software Development with MATLAB From research and prototype to production 2017 The MathWorks, Inc. 1 What Are Your Software Development Concerns? Accuracy Compatibility Cost Developer Expertise
More informationParallel Computing with MATLAB
Parallel Computing with MATLAB Jos Martin Principal Architect, Parallel Computing Tools jos.martin@mathworks.co.uk 2015 The MathWorks, Inc. 1 Overview Scene setting Task Parallel (par*) Why doesn t it
More informationBig Data con MATLAB. Lucas García The MathWorks, Inc. 1
Big Data con MATLAB Lucas García 2015 The MathWorks, Inc. 1 Agenda Introduction Remote Arrays in MATLAB Tall Arrays for Big Data Scaling up Summary 2 Architecture of an analytics system Data from instruments
More informationWhat's New in MATLAB for Engineering Data Analytics?
What's New in MATLAB for Engineering Data Analytics? Will Wilson Application Engineer MathWorks, Inc. 2017 The MathWorks, Inc. 1 Agenda Data Types Tall Arrays for Big Data Machine Learning (for Everyone)
More informationEmbarquez votre Intelligence Artificielle (IA) sur CPU, GPU et FPGA
Embarquez votre Intelligence Artificielle (IA) sur CPU, GPU et FPGA Pierre Nowodzienski Engineer pierre.nowodzienski@mathworks.fr 2018 The MathWorks, Inc. 1 From Data to Business value Make decisions Get
More informationWhat s New for MATLAB David Willingham
What s New for MATLAB David Willingham 2015 The MathWorks, Inc. 1 MATLAB Execution Engine Redesigned execution engine runs MATLAB code faster All MATLAB code is now JIT compiled A platform for future improvements
More informationDeep learning in MATLAB From Concept to CUDA Code
Deep learning in MATLAB From Concept to CUDA Code Roy Fahn Applications Engineer Systematics royf@systematics.co.il 03-7660111 Ram Kokku Principal Engineer MathWorks ram.kokku@mathworks.com 2017 The MathWorks,
More informationDi Zhao Ohio State University MVAPICH User Group (MUG) Meeting, August , Columbus Ohio
Di Zhao zhao.1029@osu.edu Ohio State University MVAPICH User Group (MUG) Meeting, August 26-27 2013, Columbus Ohio Nvidia Kepler K20X Intel Xeon Phi 7120 Launch Date November 2012 Q2 2013 Processor Per-processor
More informationScaling up MATLAB Analytics Marta Wilczkowiak, PhD Senior Applications Engineer MathWorks
Scaling up MATLAB Analytics Marta Wilczkowiak, PhD Senior Applications Engineer MathWorks 2013 The MathWorks, Inc. 1 Agenda Giving access to your analytics to more users Handling larger problems 2 When
More informationdesigning a GPU Computing Solution
designing a GPU Computing Solution Patrick Van Reeth EMEA HPC Competency Center - GPU Computing Solutions Saturday, May the 29th, 2010 1 2010 Hewlett-Packard Development Company, L.P. The information contained
More informationIntegrating Advanced Analytics with Big Data
Integrating Advanced Analytics with Big Data Ian McKenna, Ph.D. Senior Financial Engineer 2017 The MathWorks, Inc. 1 The Goal SCALE! 2 The Solution tall 3 Agenda Introduction to tall data Case Study: Predicting
More informationSystem Requirements & Platform Availability by Product for R2016b
& Platform Availability by Product for R2016b View general system requirements. Product Aerospace Blockset Requires Aerospace Control recommended Aerospace Antenna RF recommended Phased Array recommended
More informationGeneral Purpose GPU Computing in Partial Wave Analysis
JLAB at 12 GeV - INT General Purpose GPU Computing in Partial Wave Analysis Hrayr Matevosyan - NTC, Indiana University November 18/2009 COmputationAL Challenges IN PWA Rapid Increase in Available Data
More information컴퓨터비전의최신기술 : Deep Learning, 3D Vision and Embedded Vision
1 컴퓨터비전의최신기술 : Deep Learning, 3D Vision and Embedded Vision 김종남 Application Engineer 2017 The MathWorks, Inc. 2 Three Main Topics New capabilities for computer vision system design: Deep Learning 3-D Vision
More informationIntegrating MATLAB Analytics into Business-Critical Applications Marta Wilczkowiak Senior Applications Engineer MathWorks
Integrating MATLAB Analytics into Business-Critical Applications Marta Wilczkowiak Senior Applications Engineer MathWorks 2015 The MathWorks, Inc. 1 Problem statement Democratization: Is it possible to
More informationRenderscript Accelerated Advanced Image and Video Processing on ARM Mali T-600 GPUs. Lihua Zhang, Ph.D. MulticoreWare Inc.
Renderscript Accelerated Advanced Image and Video Processing on ARM Mali T-600 GPUs Lihua Zhang, Ph.D. MulticoreWare Inc. lihua@multicorewareinc.com Overview More & more mobile apps are beginning to require
More informationStream Processing with CUDA TM A Case Study Using Gamebryo's Floodgate Technology
Stream Processing with CUDA TM A Case Study Using Gamebryo's Floodgate Technology Dan Amerson, Technical Director, Emergent Game Technologies Purpose Why am I giving this talk? To answer this question:
More informationIntegrate MATLAB Analytics into Enterprise Applications
Integrate Analytics into Enterprise Applications Dr. Roland Michaely 2015 The MathWorks, Inc. 1 Data Analytics Workflow Access and Explore Data Preprocess Data Develop Predictive Models Integrate Analytics
More informationIntroduction to MATLAB application deployment
Introduction to application deployment Antti Löytynoja, Application Engineer 2015 The MathWorks, Inc. 1 Technical Computing with Products Access Explore & Create Share Options: Files Data Software Data
More informationMATLAB: The challenges involved in providing a high-level language on a GPU
MATLAB: The challenges involved in providing a high-level language on a GPU Jos Martin jos.martin@mathworks.co.uk 2013 The MathWorks, Inc. 1 Agenda Why did we introduce GPU support? What did we do? What
More informationN-Body Simulation using CUDA. CSE 633 Fall 2010 Project by Suraj Alungal Balchand Advisor: Dr. Russ Miller State University of New York at Buffalo
N-Body Simulation using CUDA CSE 633 Fall 2010 Project by Suraj Alungal Balchand Advisor: Dr. Russ Miller State University of New York at Buffalo Project plan Develop a program to simulate gravitational
More informationAutomated Trading with MATLAB Stuart Kozola Computational Finance
Automated Trading with MATLAB Stuart Kozola Computational Finance 2012 The MathWorks, Inc. 1 Challenges when developing and implementing trading strategies and systems Increasing complexity More data More
More informationG P G P U : H I G H - P E R F O R M A N C E C O M P U T I N G
Joined Advanced Student School (JASS) 2009 March 29 - April 7, 2009 St. Petersburg, Russia G P G P U : H I G H - P E R F O R M A N C E C O M P U T I N G Dmitry Puzyrev St. Petersburg State University Faculty
More informationCUDA PROGRAMMING MODEL Chaithanya Gadiyam Swapnil S Jadhav
CUDA PROGRAMMING MODEL Chaithanya Gadiyam Swapnil S Jadhav CMPE655 - Multiple Processor Systems Fall 2015 Rochester Institute of Technology Contents What is GPGPU? What s the need? CUDA-Capable GPU Architecture
More informationMATLAB AND PARALLEL COMPUTING
Image Processing & Communication, vol. 17, no. 4, pp. 207-216 DOI: 10.2478/v10248-012-0048-5 207 MATLAB AND PARALLEL COMPUTING MAGDALENA SZYMCZYK, PIOTR SZYMCZYK AGH University of Science and Technology,
More informationINTRODUCTION TO MATLAB PARALLEL COMPUTING TOOLBOX
INTRODUCTION TO MATLAB PARALLEL COMPUTING TOOLBOX Keith Ma ---------------------------------------- keithma@bu.edu Research Computing Services ----------- help@rcs.bu.edu Boston University ----------------------------------------------------
More informationTesla GPU Computing A Revolution in High Performance Computing
Tesla GPU Computing A Revolution in High Performance Computing Mark Harris, NVIDIA Agenda Tesla GPU Computing CUDA Fermi What is GPU Computing? Introduction to Tesla CUDA Architecture Programming & Memory
More informationIntroduction to MATLAB for Finance
Introduction to MATLAB for Finance Bratislava June 4, 2009 2009 The MathWorks, Inc. Jörg-M. Sautter Application Engineer The MathWorks MATLAB Benefits Solutions to access, explore, and share A seamless
More informationSEASHORE / SARUMAN. Short Read Matching using GPU Programming. Tobias Jakobi
SEASHORE SARUMAN Summary 1 / 24 SEASHORE / SARUMAN Short Read Matching using GPU Programming Tobias Jakobi Center for Biotechnology (CeBiTec) Bioinformatics Resource Facility (BRF) Bielefeld University
More informationWhat s New in MATLAB May 16, 2017
What s New in MATLAB May 16, 2017 2017 The MathWorks, Inc. 1 Agenda MATLAB Foundation Working with Data Building & Sharing MATLAB Applications Application Specific Enhancements Summary and Wrap-up 2 Agenda
More informationPervasive DataRush TM
Pervasive DataRush TM Parallel Data Analysis with KNIME www.pervasivedatarush.com Company Overview Global Software Company Tens of thousands of users across the globe Americas, EMEA, Asia ~230 employees
More informationNVIDIA DGX SYSTEMS PURPOSE-BUILT FOR AI
NVIDIA DGX SYSTEMS PURPOSE-BUILT FOR AI Overview Unparalleled Value Product Portfolio Software Platform From Desk to Data Center to Cloud Summary AI researchers depend on computing performance to gain
More informationHow Real-Time Testing Improves the Design of a PMSM Controller
How Real-Time Testing Improves the Design of a PMSM Controller Prasanna Deshpande Control Design & Automation Application Engineer MathWorks 2015 The MathWorks, Inc. 1 Problem Statement: Design speed control
More informationParticle-in-Cell Simulations on Modern Computing Platforms. Viktor K. Decyk and Tajendra V. Singh UCLA
Particle-in-Cell Simulations on Modern Computing Platforms Viktor K. Decyk and Tajendra V. Singh UCLA Outline of Presentation Abstraction of future computer hardware PIC on GPUs OpenCL and Cuda Fortran
More informationMatCL - OpenCL MATLAB Interface
MatCL - OpenCL MATLAB Interface MatCL - OpenCL MATLAB Interface Slide 1 MatCL - OpenCL MATLAB Interface OpenCL toolkit for Mathworks MATLAB/SIMULINK Compile & Run OpenCL Kernels Handles OpenCL memory management
More informationMATLAB is a multi-paradigm numerical computing environment fourth-generation programming language. A proprietary programming language developed by
1 MATLAB is a multi-paradigm numerical computing environment fourth-generation programming language. A proprietary programming language developed by MathWorks In 2004, MATLAB had around one million users
More informationCUDA Optimizations WS Intelligent Robotics Seminar. Universität Hamburg WS Intelligent Robotics Seminar Praveen Kulkarni
CUDA Optimizations WS 2014-15 Intelligent Robotics Seminar 1 Table of content 1 Background information 2 Optimizations 3 Summary 2 Table of content 1 Background information 2 Optimizations 3 Summary 3
More informationModel-Based Design for effective HW/SW Co-Design Alexander Schreiber Senior Application Engineer MathWorks, Germany
Model-Based Design for effective HW/SW Co-Design Alexander Schreiber Senior Application Engineer MathWorks, Germany 2013 The MathWorks, Inc. 1 Agenda Model-Based Design of embedded Systems Software Implementation
More informationDesigning and Targeting Video Processing Subsystems for Hardware
1 Designing and Targeting Video Processing Subsystems for Hardware 정승혁과장 Senior Application Engineer MathWorks Korea 2017 The MathWorks, Inc. 2 Pixel-stream Frame-based Process : From Algorithm to Hardware
More informationBehind Today s Trends The Technologies Driving Change. Paul Smith Director Consulting Services
Behind Today s Trends The Technologies Driving Change Paul Smith Director Consulting Services Industry 4.0 Big Data Wearable Tech Cloud Computing Internet of Things MOOC Trends from 2009 Social Computing
More informationIntroduction to Matlab GPU Acceleration for. Computational Finance. Chuan- Hsiang Han 1. Section 1: Introduction
Introduction to Matlab GPU Acceleration for Computational Finance Chuan- Hsiang Han 1 Abstract: This note aims to introduce the concept of GPU computing in Matlab and demonstrates several numerical examples
More informationIntroduction to GPU hardware and to CUDA
Introduction to GPU hardware and to CUDA Philip Blakely Laboratory for Scientific Computing, University of Cambridge Philip Blakely (LSC) GPU introduction 1 / 35 Course outline Introduction to GPU hardware
More informationWhat s New with the MATLAB and Simulink Product Families. Marta Wilczkowiak & Coorous Mohtadi Application Engineering Group
What s New with the MATLAB and Simulink Product Families Marta Wilczkowiak & Coorous Mohtadi Application Engineering Group 1 Area MATLAB Math, Statistics, and Optimization Application Deployment Parallel
More informationSDACCEL DEVELOPMENT ENVIRONMENT. The Xilinx SDAccel Development Environment. Bringing The Best Performance/Watt to the Data Center
SDAccel Environment The Xilinx SDAccel Development Environment Bringing The Best Performance/Watt to the Data Center Introduction Data center operators constantly seek more server performance. Currently
More informationNavigating Big Data with MATLAB
Navigating Big Data with MATLAB Isaac Noh Application Engineer 2015 The MathWorks, Inc. 1 How big is big? What does Big Data even mean? Big data is a term for data sets that are so large or complex that
More informationMassively Parallel Architectures
Massively Parallel Architectures A Take on Cell Processor and GPU programming Joel Falcou - LRI joel.falcou@lri.fr Bat. 490 - Bureau 104 20 janvier 2009 Motivation The CELL processor Harder,Better,Faster,Stronger
More informationWhat s New in MATLAB and Simulink The MathWorks, Inc. 1
What s New in MATLAB Simulink 2015 The MathWorks, Inc. 1 Engineers scientists 2 Engineers scientists Develop algorithms Analyze data write MATLAB code. 3 Engineers scientists deploy algorithms applications
More informationReal-Time Testing in a Modern, Agile Development Workflow
Real-Time Testing in a Modern, Agile Development Workflow Simon Eriksson Application Engineer 2015 The MathWorks, Inc. 1 Demo Going from Desktop Testing to Real-Time Testing 2 Key Take-Aways From This
More informationWhat s New in MATLAB and Simulink Young Joon Lee Principal Application Engineer
What s New in MATLAB Simulink Young Joon Lee Principal Application Engineer 2016 The MathWorks, Inc. 1 Engineers scientists 2 Engineers scientists Develop algorithms Analyze data write MATLAB code. 3 Engineers
More informationApplication Development and Deployment With MATLAB
Application Development and Deployment With Jean-Philippe Villaréal Application Engineer Applications Engineering Group MathWorks Benelux June 11, 2015 2015 The MathWorks, Inc. 1 Typical Industry Challenges
More informationDaniel D. Warner. May 31, Introduction to Parallel Matlab. Daniel D. Warner. Introduction. Matlab s 5-fold way. Basic Matlab Example
to May 31, 2010 What is Matlab? Matlab is... an Integrated Development Environment for solving numerical problems in computational science. a collection of state-of-the-art algorithms for scientific computing
More informationBlock Lanczos-Montgomery method over large prime fields with GPU accelerated dense operations
Block Lanczos-Montgomery method over large prime fields with GPU accelerated dense operations Nikolai Zamarashkin and Dmitry Zheltkov INM RAS, Gubkina 8, Moscow, Russia {nikolai.zamarashkin,dmitry.zheltkov}@gmail.com
More informationSpeed Up Your Codes Using GPU
Speed Up Your Codes Using GPU Wu Di and Yeo Khoon Seng (Department of Mechanical Engineering) The use of Graphics Processing Units (GPU) for rendering is well known, but their power for general parallel
More informationHigh-Performance Data Loading and Augmentation for Deep Neural Network Training
High-Performance Data Loading and Augmentation for Deep Neural Network Training Trevor Gale tgale@ece.neu.edu Steven Eliuk steven.eliuk@gmail.com Cameron Upright c.upright@samsung.com Roadmap 1. The General-Purpose
More informationOptimization solutions for the segmented sum algorithmic function
Optimization solutions for the segmented sum algorithmic function ALEXANDRU PÎRJAN Department of Informatics, Statistics and Mathematics Romanian-American University 1B, Expozitiei Blvd., district 1, code
More informationOptimize DSP Designs and Code using Fixed-Point Designer
Optimize DSP Designs and Code using Fixed-Point Designer MathWorks Korea 이웅재부장 Senior Application Engineer 2013 The MathWorks, Inc. 1 Agenda Fixed-point concepts Introducing Fixed-Point Designer Overview
More informationREAL PERFORMANCE RESULTS WITH VMWARE HORIZON AND VIEWPLANNER
April 4-7, 2016 Silicon Valley REAL PERFORMANCE RESULTS WITH VMWARE HORIZON AND VIEWPLANNER Manvender Rawat, NVIDIA Jason K. Lee, NVIDIA Uday Kurkure, VMware Inc. Overview of VMware Horizon 7 and NVIDIA
More informationAn Introduction to Big Data Formats
Introduction to Big Data Formats 1 An Introduction to Big Data Formats Understanding Avro, Parquet, and ORC WHITE PAPER Introduction to Big Data Formats 2 TABLE OF TABLE OF CONTENTS CONTENTS INTRODUCTION
More informationDesigning a Domain-specific Language to Simulate Particles. dan bailey
Designing a Domain-specific Language to Simulate Particles dan bailey Double Negative Largest Visual Effects studio in Europe Offices in London and Singapore Large and growing R & D team Squirt Fluid Solver
More informationLDPC Simulation With CUDA GPU
LDPC Simulation With CUDA GPU EE179 Final Project Kangping Hu June 3 rd 2014 1 1. Introduction This project is about simulating the performance of binary Low-Density-Parity-Check-Matrix (LDPC) Code with
More informationAccelerate your SAS analytics to take the gold
Accelerate your SAS analytics to take the gold A White Paper by Fuzzy Logix Whatever the nature of your business s analytics environment we are sure you are under increasing pressure to deliver more: more
More informationThe Evolution of Big Data Platforms and Data Science
IBM Analytics The Evolution of Big Data Platforms and Data Science ECC Conference 2016 Brandon MacKenzie June 13, 2016 2016 IBM Corporation Hello, I m Brandon MacKenzie. I work at IBM. Data Science - Offering
More informationAnalyzing Fleet Data with MATLAB and Spark
Analyzing Fleet Data with MATLAB and Spark Christoph Stockhammer 2018 The MathWorks, Inc. 1 What does Fleet mean? A Fleet is any group of things that can generate data and that you would like to look at
More informationGPU Programming. Lecture 1: Introduction. Miaoqing Huang University of Arkansas 1 / 27
1 / 27 GPU Programming Lecture 1: Introduction Miaoqing Huang University of Arkansas 2 / 27 Outline Course Introduction GPUs as Parallel Computers Trend and Design Philosophies Programming and Execution
More informationFramework of rcuda: An Overview
Framework of rcuda: An Overview Mohamed Hussain 1, M.B.Potdar 2, Third Viraj Choksi 3 11 Research scholar, VLSI & Embedded Systems, Gujarat Technological University, Ahmedabad, India 2 Project Director,
More informationCSE 599 I Accelerated Computing - Programming GPUS. Memory performance
CSE 599 I Accelerated Computing - Programming GPUS Memory performance GPU Teaching Kit Accelerated Computing Module 6.1 Memory Access Performance DRAM Bandwidth Objective To learn that memory bandwidth
More informationhigh performance medical reconstruction using stream programming paradigms
high performance medical reconstruction using stream programming paradigms This Paper describes the implementation and results of CT reconstruction using Filtered Back Projection on various stream programming
More information2015 The MathWorks, Inc. 1
2015 The MathWorks, Inc. 1 웨어러블디바이스의신호분석 Senior Application Engineer 김종남 2015 The MathWorks, Inc. 2 Agenda Internet Of Things Signal Analytics and Classification : On data from wareable and mobile device
More information