Adapting scientific computing problems to cloud computing frameworks Ph.D. Thesis. Pelle Jakovits

Size: px
Start display at page:

Download "Adapting scientific computing problems to cloud computing frameworks Ph.D. Thesis. Pelle Jakovits"

Transcription

1 Adapting scientific computing problems to cloud computing frameworks Ph.D. Thesis Pelle Jakovits

2 Outline Problem statement State of the art Approach Solutions and contributions Current work Conclusions 3/21/2013 2/22

3 The Problem Accurate and large-scale scientific modeling and simulation applications require large amounts of computing resources tend to run for long periods of time are complicated to create and debug Using de facto tools like MPI to create such applications takes a lot of time and resources. Have to take care of data partitioning, distribution and synchronization, deadlocks, fault recovery, etc. 3/21/2013 3/22

4 The problem in Cloud Public clouds provide very convenient access to computing resources On-demand and in real-time As long as you can afford them They are built upon commodity hardware, which means there is constant risk of hardware and network failures Thus large scale potentially long running scientific applications have to be able to handle faults. 3/21/2013 4/22

5 State of the art in 2010 In 2004 Google had published MapReduce Hadoop MapReduce had appeared in 2008 Distributed computing framework for huge scale data processing Freely usable and open source Provides automatic parallelization and fault tolerance Algorithms have to follow the MapReduce model 3/21/2013 5/22

6 MapReduce model 3/21/2013 6/22

7 Hadoop MapReduce The user only has to write Map and Reduce functions Parallelism is achieved by executing Map and Reduce tasks concurrently Framework handles everything else for the user Allows to focus on implementing the algorithms instead of managing the parallelization Fault tolerance is achieved by data replication and re-executing failed tasks. 3/21/2013 7/22

8 Goals of this study Precisely identify which algorithm characteristics affect their efficiency and scalability when adapted to the MapReduce model. Create a classification for scientific computing algorithms which would provide guidelines for deciding the suitability of new algorithms. Provide alternatives for algorithms that are not suitable for MapReduce Should still have the same or most of the advantages that MapReduce provides. 3/21/2013 8/22

9 Approach: Classification We have created a classification scientific computing algorithms, based on how they are adapted to the MapReduce model: 1. As a single MR job 2. As a constant number of MR jobs 3. Single MR job for each iteration 4. Multiple MR jobs each iteration We adapted algorithms from each of these classes to Hadoop MapReduce and investigated the results. 3/21/2013 9/22

10 Issues with Hadoop MapReduce It is designed and suitable for: Large scale data processing tasks Embarrassingly parallel tasks Has serious issues with iterative algorithms Long start up and clean up times ~17 seconds No way to keep important data in memory between MapReduce job executions At each iteration, all data is read again from HDFS and written back there at the end. Thus, there is significant overhead in every tieration 3/21/ /22

11 Approach: Alternatives We have investigated three alternative approaches for algorithms which are not suitable for the classical MapReduce model: 1. Try to restructure algorithms into non-iterative versions 2. Alternative MapReduce frameworks 3. Alternatibe distributed computing models 3/21/ /22

12 Restructuring algorithms From PAM klustering to CLARA CLARA is designed from the start to be more effective From Conjugate Gradient (CG) to Monte Carlo Matrix Inverse Result is much less effective Does not work well with sparse matrices Is generally very difficult to find an alternative that performs close to the original algoirithm Can only be applied in small number of cases Requires a great understanding of the involved algorithms and the framework it is applyed on. 3/21/ /22

13 Alternative Mapreduce frameworks Qlternative MapReduce implementations that are designed to handle iterative algorithms: Twister HaLoop Spark For example, in the case of Conjugate Gradient linear system solver (64 million non-0 element matrix): 3/21/ /22

14 Alternative Mapreduce frameworks But, as a result alternative Mapreduce frameworks often: step away from the classical MapReduce model give up advantages of the MapReduce model, Fault tolerance or multiple concurrent reduce tasks are less stable, are more complicated to use and debug 3/21/ /22

15 Alternative models Bulk Synchronous Parallel model Created by Valiant in 1990 Google gave up using MapReduce for large-scale complex graph processing Designed a new framework Pregel instead that uses Bulk Synchronous parallel model instead of MapReduce Details published in 2010 Pregel is proprietary. Like with MapReduce, third parties have designed alternative freely usable versions: jpregel Hama Giraph 3/21/ /22

16 Bulk Synchronous Parallel (BSP) Distributed computing model for iterative applications Computations consist of a sequence of super steps Superstep consists of 3 substeps: 1. Local computation 2. Sending messages to neighboring tasks, which can be accessed only at the next super step 3. Barrier synchronization 3/21/ /22

17 BSP results Comparison of different BSP (BSPonMPI and HAMA) and MPI (MPJ and MpiJava) implementations to Hadoop when clustering 80,000 objects using K-Medoid method. 3/21/ /22

18 Current work Extend the evaluation of Iterative MapReduce frameworks for scientific applications Investigate the efficiency of MapReduce applcations in more detail Karl Potisepp Investigate creating a fault tolerant BSP for scientific computing algorithms Ilja Kromonov Enhance the current classification to improve its accuracy 3/21/ /22

19 More current work Design a methodology for this approach. For classifying scientific computing algorithms For adapting them to MapReduce or the chosen alternatives Create a repository of design patterns and performance measurement results of adapting scientific computing algorithms to MapReduce and it s alternatives. 3/21/ /22

20 Even more work not directly connected to thesis Direct migration of scientific computing experiments to the cloud D2CM tool for migrating electrochemical experiments CloudML project with SINTEF Model based deployment of large scale scientific experiments Quantifying the cost of virtualization for distributed computing applications MapReduce in Image processing SAR satellite image processing in MapReduce Large scale image processing in MapReduce 3/21/ /22

21 Conclusions This study is aimed at supporting computer scientists who need to scale up scientific computing applications and who would like to know: Whether their algorithms are suitable for the MapReduce model? What is the best approach to adapt them to Hadoop or the alternative solutions? What parallel speedup and efficiency they can expect to achieve from the result? How do the results compare to a custom implementations of the same algorithms in MPI? Scalability? Parallel efficiency? 3/21/ /22

22 Thank You for your attention! Questions? 3/21/ /22

Apache Hama Design Document v0.6

Apache Hama Design Document v0.6 Apache Hama Design Document v0.6 Introduction Hama Architecture BSPMaster GroomServer Zookeeper BSP Task Execution Job Submission Job and Task Scheduling Task Execution Lifecycle Synchronization Fault

More information

BSPCloud: A Hybrid Programming Library for Cloud Computing *

BSPCloud: A Hybrid Programming Library for Cloud Computing * BSPCloud: A Hybrid Programming Library for Cloud Computing * Xiaodong Liu, Weiqin Tong and Yan Hou Department of Computer Engineering and Science Shanghai University, Shanghai, China liuxiaodongxht@qq.com,

More information

Scalability of parallel scientific applications on the cloud

Scalability of parallel scientific applications on the cloud Scientific Programming 19 (2011) 91 105 91 DOI 10.3233/SPR-2011-0320 IOS Press Scalability of parallel scientific applications on the cloud Satish Narayana Srirama, Oleg Batrashev, Pelle Jakovits and Eero

More information

Mobile & Cloud Computing: Research Challenges. Satish Srirama satish.srirama@ut.ee

Mobile & Cloud Computing: Research Challenges. Satish Srirama satish.srirama@ut.ee Mobile & Cloud Computing: Research Challenges Satish Srirama satish.srirama@ut.ee Who am I Head of Mobile & Cloud Lab, Institute of Computer Science, University of Tartu, Estonia http://mc.cs.ut.ee 1/23/2014

More information

Analysis of Web Archives. Vinay Goel Senior Data Engineer

Analysis of Web Archives. Vinay Goel Senior Data Engineer Analysis of Web Archives Vinay Goel Senior Data Engineer Internet Archive Established in 1996 501(c)(3) non profit organization 20+ PB (compressed) of publicly accessible archival material Technology partner

More information

Mizan: A System for Dynamic Load Balancing in Large-scale Graph Processing

Mizan: A System for Dynamic Load Balancing in Large-scale Graph Processing /35 Mizan: A System for Dynamic Load Balancing in Large-scale Graph Processing Zuhair Khayyat 1 Karim Awara 1 Amani Alonazi 1 Hani Jamjoom 2 Dan Williams 2 Panos Kalnis 1 1 King Abdullah University of

More information

Software tools for Complex Networks Analysis. Fabrice Huet, University of Nice Sophia- Antipolis SCALE (ex-oasis) Team

Software tools for Complex Networks Analysis. Fabrice Huet, University of Nice Sophia- Antipolis SCALE (ex-oasis) Team Software tools for Complex Networks Analysis Fabrice Huet, University of Nice Sophia- Antipolis SCALE (ex-oasis) Team MOTIVATION Why do we need tools? Source : nature.com Visualization Properties extraction

More information

BIG DATA Giraph. Felipe Caicedo December-2012. Cloud Computing & Big Data. FIB-UPC Master MEI

BIG DATA Giraph. Felipe Caicedo December-2012. Cloud Computing & Big Data. FIB-UPC Master MEI BIG DATA Giraph Cloud Computing & Big Data Felipe Caicedo December-2012 FIB-UPC Master MEI Content What is Apache Giraph? Motivation Existing solutions Features How it works Components and responsibilities

More information

Hadoop. MPDL-Frühstück 9. Dezember 2013 MPDL INTERN

Hadoop. MPDL-Frühstück 9. Dezember 2013 MPDL INTERN Hadoop MPDL-Frühstück 9. Dezember 2013 MPDL INTERN Understanding Hadoop Understanding Hadoop What's Hadoop about? Apache Hadoop project (started 2008) downloadable open-source software library (current

More information

Evaluating partitioning of big graphs

Evaluating partitioning of big graphs Evaluating partitioning of big graphs Fredrik Hallberg, Joakim Candefors, Micke Soderqvist fhallb@kth.se, candef@kth.se, mickeso@kth.se Royal Institute of Technology, Stockholm, Sweden Abstract. Distributed

More information

Challenges for Data Driven Systems

Challenges for Data Driven Systems Challenges for Data Driven Systems Eiko Yoneki University of Cambridge Computer Laboratory Quick History of Data Management 4000 B C Manual recording From tablets to papyrus to paper A. Payberah 2014 2

More information

SIAM PP 2014! MapReduce in Scientific Computing! February 19, 2014

SIAM PP 2014! MapReduce in Scientific Computing! February 19, 2014 SIAM PP 2014! MapReduce in Scientific Computing! February 19, 2014 Paul G. Constantine! Applied Math & Stats! Colorado School of Mines David F. Gleich! Computer Science! Purdue University Hans De Sterck!

More information

Overview on Graph Datastores and Graph Computing Systems. -- Litao Deng (Cloud Computing Group) 06-08-2012

Overview on Graph Datastores and Graph Computing Systems. -- Litao Deng (Cloud Computing Group) 06-08-2012 Overview on Graph Datastores and Graph Computing Systems -- Litao Deng (Cloud Computing Group) 06-08-2012 Graph - Everywhere 1: Friendship Graph 2: Food Graph 3: Internet Graph Most of the relationships

More information

HPC ABDS: The Case for an Integrating Apache Big Data Stack

HPC ABDS: The Case for an Integrating Apache Big Data Stack HPC ABDS: The Case for an Integrating Apache Big Data Stack with HPC 1st JTC 1 SGBD Meeting SDSC San Diego March 19 2014 Judy Qiu Shantenu Jha (Rutgers) Geoffrey Fox gcf@indiana.edu http://www.infomall.org

More information

Big Data Analytics. Lucas Rego Drumond

Big Data Analytics. Lucas Rego Drumond Big Data Analytics Lucas Rego Drumond Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim, Germany MapReduce II MapReduce II 1 / 33 Outline 1. Introduction

More information

ESS event: Big Data in Official Statistics. Antonino Virgillito, Istat

ESS event: Big Data in Official Statistics. Antonino Virgillito, Istat ESS event: Big Data in Official Statistics Antonino Virgillito, Istat v erbi v is 1 About me Head of Unit Web and BI Technologies, IT Directorate of Istat Project manager and technical coordinator of Web

More information

Machine Learning over Big Data

Machine Learning over Big Data Machine Learning over Big Presented by Fuhao Zou fuhao@hust.edu.cn Jue 16, 2014 Huazhong University of Science and Technology Contents 1 2 3 4 Role of Machine learning Challenge of Big Analysis Distributed

More information

Energy Efficient MapReduce

Energy Efficient MapReduce Energy Efficient MapReduce Motivation: Energy consumption is an important aspect of datacenters efficiency, the total power consumption in the united states has doubled from 2000 to 2005, representing

More information

Parallel Databases. Parallel Architectures. Parallelism Terminology 1/4/2015. Increase performance by performing operations in parallel

Parallel Databases. Parallel Architectures. Parallelism Terminology 1/4/2015. Increase performance by performing operations in parallel Parallel Databases Increase performance by performing operations in parallel Parallel Architectures Shared memory Shared disk Shared nothing closely coupled loosely coupled Parallelism Terminology Speedup:

More information

Cloud Computing Summary and Preparation for Examination

Cloud Computing Summary and Preparation for Examination Basics of Cloud Computing Lecture 8 Cloud Computing Summary and Preparation for Examination Satish Srirama Outline Quick recap of what we have learnt as part of this course How to prepare for the examination

More information

PARALLEL PROGRAMMING

PARALLEL PROGRAMMING PARALLEL PROGRAMMING TECHNIQUES AND APPLICATIONS USING NETWORKED WORKSTATIONS AND PARALLEL COMPUTERS 2nd Edition BARRY WILKINSON University of North Carolina at Charlotte Western Carolina University MICHAEL

More information

Enabling Multi-pipeline Data Transfer in HDFS for Big Data Applications

Enabling Multi-pipeline Data Transfer in HDFS for Big Data Applications Enabling Multi-pipeline Data Transfer in HDFS for Big Data Applications Liqiang (Eric) Wang, Hong Zhang University of Wyoming Hai Huang IBM T.J. Watson Research Center Background Hadoop: Apache Hadoop

More information

High Productivity Data Processing Analytics Methods with Applications

High Productivity Data Processing Analytics Methods with Applications High Productivity Data Processing Analytics Methods with Applications Dr. Ing. Morris Riedel et al. Adjunct Associate Professor School of Engineering and Natural Sciences, University of Iceland Research

More information

Big-data Analytics: Challenges and Opportunities

Big-data Analytics: Challenges and Opportunities Big-data Analytics: Challenges and Opportunities Chih-Jen Lin Department of Computer Science National Taiwan University Talk at 台 灣 資 料 科 學 愛 好 者 年 會, August 30, 2014 Chih-Jen Lin (National Taiwan Univ.)

More information

Systems Engineering II. Pramod Bhatotia TU Dresden pramod.bhatotia@tu- dresden.de

Systems Engineering II. Pramod Bhatotia TU Dresden pramod.bhatotia@tu- dresden.de Systems Engineering II Pramod Bhatotia TU Dresden pramod.bhatotia@tu- dresden.de About me! Since May 2015 2015 2012 Research Group Leader cfaed, TU Dresden PhD Student MPI- SWS Research Intern Microsoft

More information

Tackling Big Data with MATLAB Adam Filion Application Engineer MathWorks, Inc.

Tackling Big Data with MATLAB Adam Filion Application Engineer MathWorks, Inc. Tackling Big Data with MATLAB Adam Filion Application Engineer MathWorks, Inc. 2015 The MathWorks, Inc. 1 Challenges of Big Data Any collection of data sets so large and complex that it becomes difficult

More information

1 st Symposium on Colossal Data and Networking (CDAN-2016) March 18-19, 2016 Medicaps Group of Institutions, Indore, India

1 st Symposium on Colossal Data and Networking (CDAN-2016) March 18-19, 2016 Medicaps Group of Institutions, Indore, India 1 st Symposium on Colossal Data and Networking (CDAN-2016) March 18-19, 2016 Medicaps Group of Institutions, Indore, India Call for Papers Colossal Data Analysis and Networking has emerged as a de facto

More information

Apache Hadoop Ecosystem

Apache Hadoop Ecosystem Apache Hadoop Ecosystem Rim Moussa ZENITH Team Inria Sophia Antipolis DataScale project rim.moussa@inria.fr Context *large scale systems Response time (RIUD ops: one hit, OLTP) Time Processing (analytics:

More information

Cloud Computing based on the Hadoop Platform

Cloud Computing based on the Hadoop Platform Cloud Computing based on the Hadoop Platform Harshita Pandey 1 UG, Department of Information Technology RKGITW, Ghaziabad ABSTRACT In the recent years,cloud computing has come forth as the new IT paradigm.

More information

Managing large clusters resources

Managing large clusters resources Managing large clusters resources ID2210 Gautier Berthou (SICS) Big Processing with No Locality Job( /crawler/bot/jd.io/1 ) submi t Workflow Manager Compute Grid Node Job This doesn t scale. Bandwidth

More information

Hadoop Parallel Data Processing

Hadoop Parallel Data Processing MapReduce and Implementation Hadoop Parallel Data Processing Kai Shen A programming interface (two stage Map and Reduce) and system support such that: the interface is easy to program, and suitable for

More information

LARGE-SCALE GRAPH PROCESSING IN THE BIG DATA WORLD. Dr. Buğra Gedik, Ph.D.

LARGE-SCALE GRAPH PROCESSING IN THE BIG DATA WORLD. Dr. Buğra Gedik, Ph.D. LARGE-SCALE GRAPH PROCESSING IN THE BIG DATA WORLD Dr. Buğra Gedik, Ph.D. MOTIVATION Graph data is everywhere Relationships between people, systems, and the nature Interactions between people, systems,

More information

Hadoop IST 734 SS CHUNG

Hadoop IST 734 SS CHUNG Hadoop IST 734 SS CHUNG Introduction What is Big Data?? Bulk Amount Unstructured Lots of Applications which need to handle huge amount of data (in terms of 500+ TB per day) If a regular machine need to

More information

Hadoop Architecture. Part 1

Hadoop Architecture. Part 1 Hadoop Architecture Part 1 Node, Rack and Cluster: A node is simply a computer, typically non-enterprise, commodity hardware for nodes that contain data. Consider we have Node 1.Then we can add more nodes,

More information

Parallel Computing. Benson Muite. benson.muite@ut.ee http://math.ut.ee/ benson. https://courses.cs.ut.ee/2014/paralleel/fall/main/homepage

Parallel Computing. Benson Muite. benson.muite@ut.ee http://math.ut.ee/ benson. https://courses.cs.ut.ee/2014/paralleel/fall/main/homepage Parallel Computing Benson Muite benson.muite@ut.ee http://math.ut.ee/ benson https://courses.cs.ut.ee/2014/paralleel/fall/main/homepage 3 November 2014 Hadoop, Review Hadoop Hadoop History Hadoop Framework

More information

Big Data and Scripting Systems beyond Hadoop

Big Data and Scripting Systems beyond Hadoop Big Data and Scripting Systems beyond Hadoop 1, 2, ZooKeeper distributed coordination service many problems are shared among distributed systems ZooKeeper provides an implementation that solves these avoid

More information

CSE-E5430 Scalable Cloud Computing Lecture 2

CSE-E5430 Scalable Cloud Computing Lecture 2 CSE-E5430 Scalable Cloud Computing Lecture 2 Keijo Heljanko Department of Computer Science School of Science Aalto University keijo.heljanko@aalto.fi 14.9-2015 1/36 Google MapReduce A scalable batch processing

More information

The Stratosphere Big Data Analytics Platform

The Stratosphere Big Data Analytics Platform The Stratosphere Big Data Analytics Platform Amir H. Payberah Swedish Institute of Computer Science amir@sics.se June 4, 2014 Amir H. Payberah (SICS) Stratosphere June 4, 2014 1 / 44 Big Data small data

More information

Parallel Programming Map-Reduce. Needless to Say, We Need Machine Learning for Big Data

Parallel Programming Map-Reduce. Needless to Say, We Need Machine Learning for Big Data Case Study 2: Document Retrieval Parallel Programming Map-Reduce Machine Learning/Statistics for Big Data CSE599C1/STAT592, University of Washington Carlos Guestrin January 31 st, 2013 Carlos Guestrin

More information

Large-Scale Data Processing

Large-Scale Data Processing Large-Scale Data Processing Eiko Yoneki eiko.yoneki@cl.cam.ac.uk http://www.cl.cam.ac.uk/~ey204 Systems Research Group University of Cambridge Computer Laboratory 2010s: Big Data Why Big Data now? Increase

More information

MapReduce and Distributed Data Analysis. Sergei Vassilvitskii Google Research

MapReduce and Distributed Data Analysis. Sergei Vassilvitskii Google Research MapReduce and Distributed Data Analysis Google Research 1 Dealing With Massive Data 2 2 Dealing With Massive Data Polynomial Memory Sublinear RAM Sketches External Memory Property Testing 3 3 Dealing With

More information

Apache Hadoop. Alexandru Costan

Apache Hadoop. Alexandru Costan 1 Apache Hadoop Alexandru Costan Big Data Landscape No one-size-fits-all solution: SQL, NoSQL, MapReduce, No standard, except Hadoop 2 Outline What is Hadoop? Who uses it? Architecture HDFS MapReduce Open

More information

Trends and Research Opportunities in Spatial Big Data Analytics and Cloud Computing NCSU GeoSpatial Forum

Trends and Research Opportunities in Spatial Big Data Analytics and Cloud Computing NCSU GeoSpatial Forum Trends and Research Opportunities in Spatial Big Data Analytics and Cloud Computing NCSU GeoSpatial Forum Siva Ravada Senior Director of Development Oracle Spatial and MapViewer 2 Evolving Technology Platforms

More information

Bringing Big Data Modelling into the Hands of Domain Experts

Bringing Big Data Modelling into the Hands of Domain Experts Bringing Big Data Modelling into the Hands of Domain Experts David Willingham Senior Application Engineer MathWorks david.willingham@mathworks.com.au 2015 The MathWorks, Inc. 1 Data is the sword of the

More information

Spark and the Big Data Library

Spark and the Big Data Library Spark and the Big Data Library Reza Zadeh Thanks to Matei Zaharia Problem Data growing faster than processing speeds Only solution is to parallelize on large clusters» Wide use in both enterprises and

More information

Spark. Fast, Interactive, Language- Integrated Cluster Computing

Spark. Fast, Interactive, Language- Integrated Cluster Computing Spark Fast, Interactive, Language- Integrated Cluster Computing Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael Franklin, Scott Shenker, Ion Stoica UC

More information

Big Data Processing with Google s MapReduce. Alexandru Costan

Big Data Processing with Google s MapReduce. Alexandru Costan 1 Big Data Processing with Google s MapReduce Alexandru Costan Outline Motivation MapReduce programming model Examples MapReduce system architecture Limitations Extensions 2 Motivation Big Data @Google:

More information

Welcome to the unit of Hadoop Fundamentals on Hadoop architecture. I will begin with a terminology review and then cover the major components

Welcome to the unit of Hadoop Fundamentals on Hadoop architecture. I will begin with a terminology review and then cover the major components Welcome to the unit of Hadoop Fundamentals on Hadoop architecture. I will begin with a terminology review and then cover the major components of Hadoop. We will see what types of nodes can exist in a Hadoop

More information

Advanced Big Data Analytics with R and Hadoop

Advanced Big Data Analytics with R and Hadoop REVOLUTION ANALYTICS WHITE PAPER Advanced Big Data Analytics with R and Hadoop 'Big Data' Analytics as a Competitive Advantage Big Analytics delivers competitive advantage in two ways compared to the traditional

More information

Scaling Out With Apache Spark. DTL Meeting 17-04-2015 Slides based on https://www.sics.se/~amir/files/download/dic/spark.pdf

Scaling Out With Apache Spark. DTL Meeting 17-04-2015 Slides based on https://www.sics.se/~amir/files/download/dic/spark.pdf Scaling Out With Apache Spark DTL Meeting 17-04-2015 Slides based on https://www.sics.se/~amir/files/download/dic/spark.pdf Your hosts Mathijs Kattenberg Technical consultant Jeroen Schot Technical consultant

More information

More AWS and Cloud-based Research at Mobile & Cloud Lab

More AWS and Cloud-based Research at Mobile & Cloud Lab Basics of Cloud Computing Lecture 7 More AWS and Cloud-based Research at Mobile & Cloud Lab Satish Srirama Outline More Amazon Web Services How we are using cloud Cloud based Research @ Mobile & Cloud

More information

2015 The MathWorks, Inc. 1

2015 The MathWorks, Inc. 1 25 The MathWorks, Inc. 빅 데이터 및 다양한 데이터 처리 위한 MATLAB의 인터페이스 환경 및 새로운 기능 엄준상 대리 Application Engineer MathWorks 25 The MathWorks, Inc. 2 Challenges of Data Any collection of data sets so large and complex

More information

Map-Reduce for Machine Learning on Multicore

Map-Reduce for Machine Learning on Multicore Map-Reduce for Machine Learning on Multicore Chu, et al. Problem The world is going multicore New computers - dual core to 12+-core Shift to more concurrent programming paradigms and languages Erlang,

More information

Using Map-Reduce for Large Scale Analysis of Graph-Based Data

Using Map-Reduce for Large Scale Analysis of Graph-Based Data Using Map-Reduce for Large Scale Analysis of Graph-Based Data NAN GONG KTH Information and Communication Technology Master of Science Thesis Stockholm, Sweden 2011 TRITA-ICT-EX-2011:218 Using Map-Reduce

More information

Role of Cloud Computing in Big Data Analytics Using MapReduce Component of Hadoop

Role of Cloud Computing in Big Data Analytics Using MapReduce Component of Hadoop Role of Cloud Computing in Big Data Analytics Using MapReduce Component of Hadoop Kanchan A. Khedikar Department of Computer Science & Engineering Walchand Institute of Technoloy, Solapur, Maharashtra,

More information

Mining Large Datasets: Case of Mining Graph Data in the Cloud

Mining Large Datasets: Case of Mining Graph Data in the Cloud Mining Large Datasets: Case of Mining Graph Data in the Cloud Sabeur Aridhi PhD in Computer Science with Laurent d Orazio, Mondher Maddouri and Engelbert Mephu Nguifo 16/05/2014 Sabeur Aridhi Mining Large

More information

3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2016) March 10-11, 2016 VIT University, Chennai, India

3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2016) March 10-11, 2016 VIT University, Chennai, India 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2016) March 10-11, 2016 VIT University, Chennai, India Call for Papers Cloud computing has emerged as a de facto computing

More information

Large Scale Graph Processing with Apache Giraph

Large Scale Graph Processing with Apache Giraph Large Scale Graph Processing with Apache Giraph Sebastian Schelter Invited talk at GameDuell Berlin 29th May 2012 the mandatory about me slide PhD student at the Database Systems and Information Management

More information

Hadoop MapReduce and Spark. Giorgio Pedrazzi, CINECA-SCAI School of Data Analytics and Visualisation Milan, 10/06/2015

Hadoop MapReduce and Spark. Giorgio Pedrazzi, CINECA-SCAI School of Data Analytics and Visualisation Milan, 10/06/2015 Hadoop MapReduce and Spark Giorgio Pedrazzi, CINECA-SCAI School of Data Analytics and Visualisation Milan, 10/06/2015 Outline Hadoop Hadoop Import data on Hadoop Spark Spark features Scala MLlib MLlib

More information

CIS 4930/6930 Spring 2014 Introduction to Data Science /Data Intensive Computing. University of Florida, CISE Department Prof.

CIS 4930/6930 Spring 2014 Introduction to Data Science /Data Intensive Computing. University of Florida, CISE Department Prof. CIS 4930/6930 Spring 2014 Introduction to Data Science /Data Intensie Computing Uniersity of Florida, CISE Department Prof. Daisy Zhe Wang Map/Reduce: Simplified Data Processing on Large Clusters Parallel/Distributed

More information

Log Mining Based on Hadoop s Map and Reduce Technique

Log Mining Based on Hadoop s Map and Reduce Technique Log Mining Based on Hadoop s Map and Reduce Technique ABSTRACT: Anuja Pandit Department of Computer Science, anujapandit25@gmail.com Amruta Deshpande Department of Computer Science, amrutadeshpande1991@gmail.com

More information

Hadoop: A Framework for Data- Intensive Distributed Computing. CS561-Spring 2012 WPI, Mohamed Y. Eltabakh

Hadoop: A Framework for Data- Intensive Distributed Computing. CS561-Spring 2012 WPI, Mohamed Y. Eltabakh 1 Hadoop: A Framework for Data- Intensive Distributed Computing CS561-Spring 2012 WPI, Mohamed Y. Eltabakh 2 What is Hadoop? Hadoop is a software framework for distributed processing of large datasets

More information

A Professional Big Data Master s Program to train Computational Specialists

A Professional Big Data Master s Program to train Computational Specialists A Professional Big Data Master s Program to train Computational Specialists Anoop Sarkar, Fred Popowich, Alexandra Fedorova! School of Computing Science! Education for Employable Graduates: Critical Questions

More information

Architectural patterns for building real time applications with Apache HBase. Andrew Purtell Committer and PMC, Apache HBase

Architectural patterns for building real time applications with Apache HBase. Andrew Purtell Committer and PMC, Apache HBase Architectural patterns for building real time applications with Apache HBase Andrew Purtell Committer and PMC, Apache HBase Who am I? Distributed systems engineer Principal Architect in the Big Data Platform

More information

A Simultaneous Solution for General Linear Equations on a Ring or Hierarchical Cluster

A Simultaneous Solution for General Linear Equations on a Ring or Hierarchical Cluster Acta Technica Jaurinensis Vol. 3. No. 1. 010 A Simultaneous Solution for General Linear Equations on a Ring or Hierarchical Cluster G. Molnárka, N. Varjasi Széchenyi István University Győr, Hungary, H-906

More information

MapReduce and Hadoop Distributed File System V I J A Y R A O

MapReduce and Hadoop Distributed File System V I J A Y R A O MapReduce and Hadoop Distributed File System 1 V I J A Y R A O The Context: Big-data Man on the moon with 32KB (1969); my laptop had 2GB RAM (2009) Google collects 270PB data in a month (2007), 20000PB

More information

Extending Hadoop beyond MapReduce

Extending Hadoop beyond MapReduce Extending Hadoop beyond MapReduce Mahadev Konar Co-Founder @mahadevkonar (@hortonworks) Page 1 Bio Apache Hadoop since 2006 - committer and PMC member Developed and supported Map Reduce @Yahoo! - Core

More information

Cloud Computing at Google. Architecture

Cloud Computing at Google. Architecture Cloud Computing at Google Google File System Web Systems and Algorithms Google Chris Brooks Department of Computer Science University of San Francisco Google has developed a layered system to handle webscale

More information

Leveraging BlobSeer to boost up the deployment and execution of Hadoop applications in Nimbus cloud environments on Grid 5000

Leveraging BlobSeer to boost up the deployment and execution of Hadoop applications in Nimbus cloud environments on Grid 5000 Leveraging BlobSeer to boost up the deployment and execution of Hadoop applications in Nimbus cloud environments on Grid 5000 Alexandra Carpen-Amarie Diana Moise Bogdan Nicolae KerData Team, INRIA Outline

More information

Introduction to Hadoop. New York Oracle User Group Vikas Sawhney

Introduction to Hadoop. New York Oracle User Group Vikas Sawhney Introduction to Hadoop New York Oracle User Group Vikas Sawhney GENERAL AGENDA Driving Factors behind BIG-DATA NOSQL Database 2014 Database Landscape Hadoop Architecture Map/Reduce Hadoop Eco-system Hadoop

More information

Big Data. White Paper. Big Data Executive Overview WP-BD-10312014-01. Jafar Shunnar & Dan Raver. Page 1 Last Updated 11-10-2014

Big Data. White Paper. Big Data Executive Overview WP-BD-10312014-01. Jafar Shunnar & Dan Raver. Page 1 Last Updated 11-10-2014 White Paper Big Data Executive Overview WP-BD-10312014-01 By Jafar Shunnar & Dan Raver Page 1 Last Updated 11-10-2014 Table of Contents Section 01 Big Data Facts Page 3-4 Section 02 What is Big Data? Page

More information

Analytics in the Cloud. Peter Sirota, GM Elastic MapReduce

Analytics in the Cloud. Peter Sirota, GM Elastic MapReduce Analytics in the Cloud Peter Sirota, GM Elastic MapReduce Data-Driven Decision Making Data is the new raw material for any business on par with capital, people, and labor. What is Big Data? Terabytes of

More information

Hadoop SNS. renren.com. Saturday, December 3, 11

Hadoop SNS. renren.com. Saturday, December 3, 11 Hadoop SNS renren.com Saturday, December 3, 11 2.2 190 40 Saturday, December 3, 11 Saturday, December 3, 11 Saturday, December 3, 11 Saturday, December 3, 11 Saturday, December 3, 11 Saturday, December

More information

Lecture 10 - Functional programming: Hadoop and MapReduce

Lecture 10 - Functional programming: Hadoop and MapReduce Lecture 10 - Functional programming: Hadoop and MapReduce Sohan Dharmaraja Sohan Dharmaraja Lecture 10 - Functional programming: Hadoop and MapReduce 1 / 41 For today Big Data and Text analytics Functional

More information

Mobile Storage and Search Engine of Information Oriented to Food Cloud

Mobile Storage and Search Engine of Information Oriented to Food Cloud Advance Journal of Food Science and Technology 5(10): 1331-1336, 2013 ISSN: 2042-4868; e-issn: 2042-4876 Maxwell Scientific Organization, 2013 Submitted: May 29, 2013 Accepted: July 04, 2013 Published:

More information

Twister4Azure: Data Analytics in the Cloud

Twister4Azure: Data Analytics in the Cloud Twister4Azure: Data Analytics in the Cloud Thilina Gunarathne, Xiaoming Gao and Judy Qiu, Indiana University Genome-scale data provided by next generation sequencing (NGS) has made it possible to identify

More information

Convex Optimization for Big Data: Lecture 2: Frameworks for Big Data Analytics

Convex Optimization for Big Data: Lecture 2: Frameworks for Big Data Analytics Convex Optimization for Big Data: Lecture 2: Frameworks for Big Data Analytics Sabeur Aridhi Aalto University, Finland Sabeur Aridhi Frameworks for Big Data Analytics 1 / 59 Introduction Contents 1 Introduction

More information

Conjugating data mood and tenses: Simple past, infinite present, fast continuous, simpler imperative, conditional future perfect

Conjugating data mood and tenses: Simple past, infinite present, fast continuous, simpler imperative, conditional future perfect Matteo Migliavacca (mm53@kent) School of Computing Conjugating data mood and tenses: Simple past, infinite present, fast continuous, simpler imperative, conditional future perfect Simple past - Traditional

More information

Case Study : 3 different hadoop cluster deployments

Case Study : 3 different hadoop cluster deployments Case Study : 3 different hadoop cluster deployments Lee moon soo moon@nflabs.com HDFS as a Storage Last 4 years, our HDFS clusters, stored Customer 1500 TB+ data safely served 375,000 TB+ data to customer

More information

BIG DATA USING HADOOP

BIG DATA USING HADOOP + Breakaway Session By Johnson Iyilade, Ph.D. University of Saskatchewan, Canada 23-July, 2015 BIG DATA USING HADOOP + Outline n Framing the Problem Hadoop Solves n Meet Hadoop n Storage with HDFS n Data

More information

Open Access Research of Fast Search Algorithm Based on Hadoop Cloud Platform

Open Access Research of Fast Search Algorithm Based on Hadoop Cloud Platform Send Orders for Reprints to reprints@benthamscience.ae The Open Automation and Control Systems Journal, 2015, 7, 1153-1159 1153 Open Access Research of Fast Search Algorithm Based on Hadoop Cloud Platform

More information

MapReduce and Hadoop Distributed File System

MapReduce and Hadoop Distributed File System MapReduce and Hadoop Distributed File System 1 B. RAMAMURTHY Contact: Dr. Bina Ramamurthy CSE Department University at Buffalo (SUNY) bina@buffalo.edu http://www.cse.buffalo.edu/faculty/bina Partially

More information

Bayesian Machine Learning (ML): Modeling And Inference in Big Data. Zhuhua Cai Google, Rice University caizhua@gmail.com

Bayesian Machine Learning (ML): Modeling And Inference in Big Data. Zhuhua Cai Google, Rice University caizhua@gmail.com Bayesian Machine Learning (ML): Modeling And Inference in Big Data Zhuhua Cai Google Rice University caizhua@gmail.com 1 Syllabus Bayesian ML Concepts (Today) Bayesian ML on MapReduce (Next morning) Bayesian

More information

Architectures for Big Data Analytics A database perspective

Architectures for Big Data Analytics A database perspective Architectures for Big Data Analytics A database perspective Fernando Velez Director of Product Management Enterprise Information Management, SAP June 2013 Outline Big Data Analytics Requirements Spectrum

More information

Lecture 32 Big Data. 1. Big Data problem 2. Why the excitement about big data 3. What is MapReduce 4. What is Hadoop 5. Get started with Hadoop

Lecture 32 Big Data. 1. Big Data problem 2. Why the excitement about big data 3. What is MapReduce 4. What is Hadoop 5. Get started with Hadoop Lecture 32 Big Data 1. Big Data problem 2. Why the excitement about big data 3. What is MapReduce 4. What is Hadoop 5. Get started with Hadoop 1 2 Big Data Problems Data explosion Data from users on social

More information

A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM

A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM Sneha D.Borkar 1, Prof.Chaitali S.Surtakar 2 Student of B.E., Information Technology, J.D.I.E.T, sborkar95@gmail.com Assistant Professor, Information

More information

Probabilistic Models for Big Data. Alex Davies and Roger Frigola University of Cambridge 13th February 2014

Probabilistic Models for Big Data. Alex Davies and Roger Frigola University of Cambridge 13th February 2014 Probabilistic Models for Big Data Alex Davies and Roger Frigola University of Cambridge 13th February 2014 The State of Big Data Why probabilistic models for Big Data? 1. If you don t have to worry about

More information

Unified Big Data Processing with Apache Spark. Matei Zaharia @matei_zaharia

Unified Big Data Processing with Apache Spark. Matei Zaharia @matei_zaharia Unified Big Data Processing with Apache Spark Matei Zaharia @matei_zaharia What is Apache Spark? Fast & general engine for big data processing Generalizes MapReduce model to support more types of processing

More information

Contents. Preface Acknowledgements. Chapter 1 Introduction 1.1

Contents. Preface Acknowledgements. Chapter 1 Introduction 1.1 Preface xi Acknowledgements xv Chapter 1 Introduction 1.1 1.1 Cloud Computing at a Glance 1.1 1.1.1 The Vision of Cloud Computing 1.2 1.1.2 Defining a Cloud 1.4 1.1.3 A Closer Look 1.6 1.1.4 Cloud Computing

More information

Introduction to DISC and Hadoop

Introduction to DISC and Hadoop Introduction to DISC and Hadoop Alice E. Fischer April 24, 2009 Alice E. Fischer DISC... 1/20 1 2 History Hadoop provides a three-layer paradigm Alice E. Fischer DISC... 2/20 Parallel Computing Past and

More information

Analysis of MapReduce Algorithms

Analysis of MapReduce Algorithms Analysis of MapReduce Algorithms Harini Padmanaban Computer Science Department San Jose State University San Jose, CA 95192 408-924-1000 harini.gomadam@gmail.com ABSTRACT MapReduce is a programming model

More information

Fault Tolerance in Hadoop for Work Migration

Fault Tolerance in Hadoop for Work Migration 1 Fault Tolerance in Hadoop for Work Migration Shivaraman Janakiraman Indiana University Bloomington ABSTRACT Hadoop is a framework that runs applications on large clusters which are built on numerous

More information

Graph Mining on Big Data System. Presented by Hefu Chai, Rui Zhang, Jian Fang

Graph Mining on Big Data System. Presented by Hefu Chai, Rui Zhang, Jian Fang Graph Mining on Big Data System Presented by Hefu Chai, Rui Zhang, Jian Fang Outline * Overview * Approaches & Environment * Results * Observations * Notes * Conclusion Overview * What we have done? *

More information

Building Out Your Cloud-Ready Solutions. Clark D. Richey, Jr., Principal Technologist, DoD

Building Out Your Cloud-Ready Solutions. Clark D. Richey, Jr., Principal Technologist, DoD Building Out Your Cloud-Ready Solutions Clark D. Richey, Jr., Principal Technologist, DoD Slide 1 Agenda Define the problem Explore important aspects of Cloud deployments Wrap up and questions Slide 2

More information

YARN, the Apache Hadoop Platform for Streaming, Realtime and Batch Processing

YARN, the Apache Hadoop Platform for Streaming, Realtime and Batch Processing YARN, the Apache Hadoop Platform for Streaming, Realtime and Batch Processing Eric Charles [http://echarles.net] @echarles Datalayer [http://datalayer.io] @datalayerio FOSDEM 02 Feb 2014 NoSQL DevRoom

More information

FutureGrid Education: Using Case Studies to Develop A Curriculum for Communicating Parallel and Distributed Computing Concepts

FutureGrid Education: Using Case Studies to Develop A Curriculum for Communicating Parallel and Distributed Computing Concepts FutureGrid Education: Using Case Studies to Develop A Curriculum for Communicating Parallel and Distributed Computing Concepts Jerome E. Mitchell, Judy Qiu, Massimo Canonio, Shantenu Jha, Linda Hayden,

More information

A tour on big data classification: Learning algorithms, Feature selection, and Imbalanced Classes

A tour on big data classification: Learning algorithms, Feature selection, and Imbalanced Classes A tour on big data classification: Learning algorithms, Feature selection, and Imbalanced Classes Francisco Herrera Research Group on Soft Computing and Information Intelligent Systems (SCI 2 S) Dept.

More information

BIG DATA AND ANALYTICS

BIG DATA AND ANALYTICS BIG DATA AND ANALYTICS Björn Bjurling, bgb@sics.se Daniel Gillblad, dgi@sics.se Anders Holst, aho@sics.se Swedish Institute of Computer Science AGENDA What is big data and analytics? and why one must bother

More information

P013 INTRODUCING A NEW GENERATION OF RESERVOIR SIMULATION SOFTWARE

P013 INTRODUCING A NEW GENERATION OF RESERVOIR SIMULATION SOFTWARE 1 P013 INTRODUCING A NEW GENERATION OF RESERVOIR SIMULATION SOFTWARE JEAN-MARC GRATIEN, JEAN-FRANÇOIS MAGRAS, PHILIPPE QUANDALLE, OLIVIER RICOIS 1&4, av. Bois-Préau. 92852 Rueil Malmaison Cedex. France

More information

Big Graph Analytics on Neo4j with Apache Spark. Michael Hunger Original work by Kenny Bastani Berlin Buzzwords, Open Stage

Big Graph Analytics on Neo4j with Apache Spark. Michael Hunger Original work by Kenny Bastani Berlin Buzzwords, Open Stage Big Graph Analytics on Neo4j with Apache Spark Michael Hunger Original work by Kenny Bastani Berlin Buzzwords, Open Stage My background I only make it to the Open Stages :) Probably because Apache Neo4j

More information