Parallel Computing. Benson Muite. benson.
|
|
- Erik Austin
- 8 years ago
- Views:
Transcription
1 Parallel Computing Benson Muite benson 3 November 2014
2 Hadoop, Review
3 Hadoop Hadoop History Hadoop Framework Fault Tolerance Example Applications
4 Hadoop History Motivated by Dean and Ghemawat MapReduce: Simplified Data Processing on Large Clusters Proc. OSDI 2004 also Communications of the ACM, 51(1), (2008) Paper describes Googles original implementation in C++ Based on this, Hadoop Developed by Doug Cutting (at Yahoo) and Mike Cafarella (graduate student at University of Washington now faculty at University of Michigan) Hadoop originally used to support Nutch search engine project Open source because needed a large number of developers Currently an Apache Foundation supported project Used by many companies for data analysis Hadoop uses Java as underlying language (efficiency concerns compared to C++, but wider number of possible users?)
5 Hadoop Framework Primarily aimed at data intensive computing Large scale databases on commodity hardware Slow network, typically ethernet, but reasonable fast commodity processors Distributed file system (Hadoop distributed file system - HDFS) Hadoop 2 is current release and has some backwards compatibility with Hadoop 1 Main feature is a map phase where local work is done without any communication, and then a reduce phase where communication is done. Main idea is that data is stored on local disks, so not required to move it over a slow network, only move results of query in map phase. For many problems, query results have much less data, so can give interactive response for large data sets over a low bandwidth high latency network.
6 Fault Tolerance Data is typically replicated thrice On primary disk, data is replicated twice to minimize access times. Third replication is done on a separate disk. System has a task scheduler (in Hadoop 1, this is not replicated, but in Hadoop 2, replicated to prevent system crashes) and then work coordinators which report back to task scheduler Should a part of the system fail, work is rescheduled There is also a lookup table or set of lookup tables Scheduling policy needs to allocate work to enable all jobs in an organization to be run efficiently. This is a tough problem to solve exactly, heuristics typically used.
7 Example Applications Search engines Examining computer internet access logs Online store purchase recommendations Credit reports Airline flight scheduling Consumer spending pattern analysis Machine translation Machine learning
8 Ways to Get Involved Part of an open source ecosystem Many developers from around the world Many extensions being proposed using the same model Targeted towards commodity hardware Some good redundancy ideas that may be useful for future exascale compute models
9 Review
10 Supercomputer Rankings Top 500 Green 500 Graph 500 Green Graph 500
11 Parallel Programming APIs OpenMP MPI OpenCL CAF UPC Many others, but the ones listed are mainstream at the moment
12 Performance measurement How long does your code take to run How well does your code utilize the hardware it is running on Amdhal s law Gustafsson s law
13 Roofline Model Upper bound for performance of your code or important kernels in your code Determine if algorithm is RAM bandwidth or compute bound at a node level Can extend ideas to system level by using interprocessor bandwidth rather than bandwidth from RAM
14 Measures of Efficiency Speedup Parallel Efficiency
15 Computer Chip Architecture Memory hierarchy: Registers, L1 Cache, L2 Cache, L3 Cache, RAM, disk Floating point units, integer units, instruction units, vector units, Fused multiply add, prefetching, cores, NUMA Clock speed
16 Computer Interconnect Toplogies Bus Ring Mesh All connected to All Hypercube 2D, 3D, 4D, 5D, 6D torii Fat Tree CLOS Dragonfly
17 Algorithm Operation Counts Dot-Product Matrix multiplication
18 Algorithm Runtime Performance Estimation Data processing: Functional operation count Operations per cycle cycles per second Data movement: Theory, Reduction, All-to-All on different simple network topologies Data movement: In practice, empirical models needed due to complexity of current networks Data movement: Theory, Switch
19 Single Core Optimization In many cases where one is doing parallelization to speed up time to solution, check whether re-optimizing your code will help Many people who re-program for Xeon Phi, make their codes faster for Xeon chip as well since they make it easier for the chip to do in order executions
20 Accelerators Good for floating point computations High energy efficiency Typically very many small processing units which do only a few things very efficiently (e.g. floating point operations) Very simple instruction scheduling Example: Xeon Phi, GPUs (Graphics Processing Unit), MPPA (Massively Parallel Processor Array), FPGA (Field Programmable Gate Array)
21 Vectorization Lowest level of parallelization Single instruction multiple data Heavily used in efficient floating point architectures such as Xeon Phi Requires regular memory accesses
22 Loop Parallelization Can be coarse grain or fine grain For many tasks, fine grain needs to be carefully implemented to avoid excessive overhead costs Usually easiest form of parallelizm to add to a code
23 Task Parallelization Often used in information processing applications Need to schedule appropriately to ensure available resources are used Need to ensure all data is available
24 DAG scheduling Method to expose parallelizm by determining task dependencies Heavily used in information processing applications Being applied to massively concurrent architectures in high performance computing Need to be careful in implementation to avoid high memory costs from generating the graph Just in time dependency generation is one way of reducing memory requirements
25 Load Balancing Need to be careful in how partition work Want all processes to be kept busy during parallel execution Static load balancing can be done easily at startup time if the program allows for this Dynamic load balancing more difficult, typically arises in adaptive solution of models for physical processes
26 Sorting Many ways to do this in parallel, examples include quick sort, merge sort Typically does not require many floating point operations, instead doing comparisons Used in bioinformatics, particle based algorithms, etc.
27 I/O Usually slowest operation File systems are complicated For many scientific computing applications, try to minimize this For many data applications, this is the most important part of the problem
28 Hadoop and Mapreduce Parallel programming model, primarily aimed for data processing Few commands which allow easy buildup of parallel programs
29 Other Topics Pipelining Vector processors (NEC SX-ACE) Non-traditional uses of supercomputing: text analysis, economic planning
30 References Herodotou, H. Automatic Tuning of Data-Intensive Analytical Workloads PhD Thesis, Duke University (2012), hero/research.html Dean, J. and Gehmawat, S. MapReduce: Simplified Data Processing on Large Clusters Proc. OSDI 04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, USA, December, (2004) also Communications of the ACM, 51(1), (2008)
31 References hadoop-tutorial-series/ Wadkar, S., Siddalingaiah, M. and Venner, J. Pro Apache Hadoop 2nd Ed. Apress (2014) Shenoy, A. Hadoop Explained Packt Publishing (2014) Nielsen, L. Hadoop for Laymen Newstreet Communications (2014) Lockwood, G.L. Tutorials in Data-Intensive Computing (2014)
32 Myroslava Stavnycha Acknowledgements
Outline. High Performance Computing (HPC) Big Data meets HPC. Case Studies: Some facts about Big Data Technologies HPC and Big Data converging
Outline High Performance Computing (HPC) Towards exascale computing: a brief history Challenges in the exascale era Big Data meets HPC Some facts about Big Data Technologies HPC and Big Data converging
More informationCSE-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 informationScalability and Classifications
Scalability and Classifications 1 Types of Parallel Computers MIMD and SIMD classifications shared and distributed memory multicomputers distributed shared memory computers 2 Network Topologies static
More informationA 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 informationIntroduction to Hadoop
Introduction to Hadoop 1 What is Hadoop? the big data revolution extracting value from data cloud computing 2 Understanding MapReduce the word count problem more examples MCS 572 Lecture 24 Introduction
More informationBIG DATA, MAPREDUCE & HADOOP
BIG, MAPREDUCE & HADOOP LARGE SCALE DISTRIBUTED SYSTEMS By Jean-Pierre Lozi A tutorial for the LSDS class LARGE SCALE DISTRIBUTED SYSTEMS BIG, MAPREDUCE & HADOOP 1 OBJECTIVES OF THIS LAB SESSION The LSDS
More informationOpen source software framework designed for storage and processing of large scale data on clusters of commodity hardware
Open source software framework designed for storage and processing of large scale data on clusters of commodity hardware Created by Doug Cutting and Mike Carafella in 2005. Cutting named the program after
More informationEnergy 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 informationBig Data and Apache Hadoop s MapReduce
Big Data and Apache Hadoop s MapReduce Michael Hahsler Computer Science and Engineering Southern Methodist University January 23, 2012 Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, 2012 1 / 23
More informationCOMP 422, Lecture 3: Physical Organization & Communication Costs in Parallel Machines (Sections 2.4 & 2.5 of textbook)
COMP 422, Lecture 3: Physical Organization & Communication Costs in Parallel Machines (Sections 2.4 & 2.5 of textbook) Vivek Sarkar Department of Computer Science Rice University vsarkar@rice.edu COMP
More informationArchitectures 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 informationBig Data With Hadoop
With Saurabh Singh singh.903@osu.edu The Ohio State University February 11, 2016 Overview 1 2 3 Requirements Ecosystem Resilient Distributed Datasets (RDDs) Example Code vs Mapreduce 4 5 Source: [Tutorials
More informationScalable Cloud Computing Solutions for Next Generation Sequencing Data
Scalable Cloud Computing Solutions for Next Generation Sequencing Data Matti Niemenmaa 1, Aleksi Kallio 2, André Schumacher 1, Petri Klemelä 2, Eija Korpelainen 2, and Keijo Heljanko 1 1 Department of
More informationPerformance Evaluation of NAS Parallel Benchmarks on Intel Xeon Phi
Performance Evaluation of NAS Parallel Benchmarks on Intel Xeon Phi ICPP 6 th International Workshop on Parallel Programming Models and Systems Software for High-End Computing October 1, 2013 Lyon, France
More informationBig Fast Data Hadoop acceleration with Flash. June 2013
Big Fast Data Hadoop acceleration with Flash June 2013 Agenda The Big Data Problem What is Hadoop Hadoop and Flash The Nytro Solution Test Results The Big Data Problem Big Data Output Facebook Traditional
More informationIntroduction to Cloud Computing
Introduction to Cloud Computing Parallel Processing I 15 319, spring 2010 7 th Lecture, Feb 2 nd Majd F. Sakr Lecture Motivation Concurrency and why? Different flavors of parallel computing Get the basic
More informationBIG 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 informationIntroduction to Big Data! with Apache Spark" UC#BERKELEY#
Introduction to Big Data! with Apache Spark" UC#BERKELEY# This Lecture" The Big Data Problem" Hardware for Big Data" Distributing Work" Handling Failures and Slow Machines" Map Reduce and Complex Jobs"
More informationWelcome 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 informationData-Intensive Programming. Timo Aaltonen Department of Pervasive Computing
Data-Intensive Programming Timo Aaltonen Department of Pervasive Computing Data-Intensive Programming Lecturer: Timo Aaltonen University Lecturer timo.aaltonen@tut.fi Assistants: Henri Terho and Antti
More informationBenchmark Hadoop and Mars: MapReduce on cluster versus on GPU
Benchmark Hadoop and Mars: MapReduce on cluster versus on GPU Heshan Li, Shaopeng Wang The Johns Hopkins University 3400 N. Charles Street Baltimore, Maryland 21218 {heshanli, shaopeng}@cs.jhu.edu 1 Overview
More informationWhat is Analytic Infrastructure and Why Should You Care?
What is Analytic Infrastructure and Why Should You Care? Robert L Grossman University of Illinois at Chicago and Open Data Group grossman@uic.edu ABSTRACT We define analytic infrastructure to be the services,
More informationDistributed File System. MCSN N. Tonellotto Complements of Distributed Enabling Platforms
Distributed File System 1 How do we get data to the workers? NAS Compute Nodes SAN 2 Distributed File System Don t move data to workers move workers to the data! Store data on the local disks of nodes
More informationMapReduce 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 informationHadoop 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 informationWrite a technical report Present your results Write a workshop/conference paper (optional) Could be a real system, simulation and/or theoretical
Identify a problem Review approaches to the problem Propose a novel approach to the problem Define, design, prototype an implementation to evaluate your approach Could be a real system, simulation and/or
More informationHadoop and Map-Reduce. Swati Gore
Hadoop and Map-Reduce Swati Gore Contents Why Hadoop? Hadoop Overview Hadoop Architecture Working Description Fault Tolerance Limitations Why Map-Reduce not MPI Distributed sort Why Hadoop? Existing Data
More informationA Study on Workload Imbalance Issues in Data Intensive Distributed Computing
A Study on Workload Imbalance Issues in Data Intensive Distributed Computing Sven Groot 1, Kazuo Goda 1, and Masaru Kitsuregawa 1 University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan Abstract.
More informationParallel 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 informationTHE HADOOP DISTRIBUTED FILE SYSTEM
THE HADOOP DISTRIBUTED FILE SYSTEM Konstantin Shvachko, Hairong Kuang, Sanjay Radia, Robert Chansler Presented by Alexander Pokluda October 7, 2013 Outline Motivation and Overview of Hadoop Architecture,
More informationChapter 2 Parallel Architecture, Software And Performance
Chapter 2 Parallel Architecture, Software And Performance UCSB CS140, T. Yang, 2014 Modified from texbook slides Roadmap Parallel hardware Parallel software Input and output Performance Parallel program
More informationBinary search tree with SIMD bandwidth optimization using SSE
Binary search tree with SIMD bandwidth optimization using SSE Bowen Zhang, Xinwei Li 1.ABSTRACT In-memory tree structured index search is a fundamental database operation. Modern processors provide tremendous
More informationCSE 590: Special Topics Course ( Supercomputing ) Lecture 10 ( MapReduce& Hadoop)
CSE 590: Special Topics Course ( Supercomputing ) Lecture 10 ( MapReduce& Hadoop) Rezaul A. Chowdhury Department of Computer Science SUNY Stony Brook Spring 2016 MapReduce MapReduce is a programming model
More informationIntroduction to Hadoop
1 What is Hadoop? Introduction to Hadoop We are living in an era where large volumes of data are available and the problem is to extract meaning from the data avalanche. The goal of the software tools
More informationHPC Programming Framework Research Team
HPC Programming Framework Research Team 1. Team Members Naoya Maruyama (Team Leader) Motohiko Matsuda (Research Scientist) Soichiro Suzuki (Technical Staff) Mohamed Wahib (Postdoctoral Researcher) Shinichiro
More informationPetascale Software Challenges. William Gropp www.cs.illinois.edu/~wgropp
Petascale Software Challenges William Gropp www.cs.illinois.edu/~wgropp Petascale Software Challenges Why should you care? What are they? Which are different from non-petascale? What has changed since
More informationChapter 11 Map-Reduce, Hadoop, HDFS, Hbase, MongoDB, Apache HIVE, and Related
Chapter 11 Map-Reduce, Hadoop, HDFS, Hbase, MongoDB, Apache HIVE, and Related Summary Xiangzhe Li Nowadays, there are more and more data everyday about everything. For instance, here are some of the astonishing
More informationLecture 5: GFS & HDFS! Claudia Hauff (Web Information Systems)! ti2736b-ewi@tudelft.nl
Big Data Processing, 2014/15 Lecture 5: GFS & HDFS!! Claudia Hauff (Web Information Systems)! ti2736b-ewi@tudelft.nl 1 Course content Introduction Data streams 1 & 2 The MapReduce paradigm Looking behind
More informationKeywords: Big Data, HDFS, Map Reduce, Hadoop
Volume 5, Issue 7, July 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Configuration Tuning
More informationIntroduction to Hadoop HDFS and Ecosystems. Slides credits: Cloudera Academic Partners Program & Prof. De Liu, MSBA 6330 Harvesting Big Data
Introduction to Hadoop HDFS and Ecosystems ANSHUL MITTAL Slides credits: Cloudera Academic Partners Program & Prof. De Liu, MSBA 6330 Harvesting Big Data Topics The goal of this presentation is to give
More informationInternational Journal of Advance Research in Computer Science and Management Studies
Volume 2, Issue 8, August 2014 ISSN: 2321 7782 (Online) International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
More informationExploring the Efficiency of Big Data Processing with Hadoop MapReduce
Exploring the Efficiency of Big Data Processing with Hadoop MapReduce Brian Ye, Anders Ye School of Computer Science and Communication (CSC), Royal Institute of Technology KTH, Stockholm, Sweden Abstract.
More informationHDFS Space Consolidation
HDFS Space Consolidation Aastha Mehta*,1,2, Deepti Banka*,1,2, Kartheek Muthyala*,1,2, Priya Sehgal 1, Ajay Bakre 1 *Student Authors 1 Advanced Technology Group, NetApp Inc., Bangalore, India 2 Birla Institute
More informationSnapshots in Hadoop Distributed File System
Snapshots in Hadoop Distributed File System Sameer Agarwal UC Berkeley Dhruba Borthakur Facebook Inc. Ion Stoica UC Berkeley Abstract The ability to take snapshots is an essential functionality of any
More informationA STUDY ON HADOOP ARCHITECTURE FOR BIG DATA ANALYTICS
A STUDY ON HADOOP ARCHITECTURE FOR BIG DATA ANALYTICS Dr. Ananthi Sheshasayee 1, J V N Lakshmi 2 1 Head Department of Computer Science & Research, Quaid-E-Millath Govt College for Women, Chennai, (India)
More informationParallel Programming Survey
Christian Terboven 02.09.2014 / Aachen, Germany Stand: 26.08.2014 Version 2.3 IT Center der RWTH Aachen University Agenda Overview: Processor Microarchitecture Shared-Memory
More informationSupercomputing and Big Data: Where are the Real Boundaries and Opportunities for Synergy?
HPC2012 Workshop Cetraro, Italy Supercomputing and Big Data: Where are the Real Boundaries and Opportunities for Synergy? Bill Blake CTO Cray, Inc. The Big Data Challenge Supercomputing minimizes data
More informationJournal of science STUDY ON REPLICA MANAGEMENT AND HIGH AVAILABILITY IN HADOOP DISTRIBUTED FILE SYSTEM (HDFS)
Journal of science e ISSN 2277-3290 Print ISSN 2277-3282 Information Technology www.journalofscience.net STUDY ON REPLICA MANAGEMENT AND HIGH AVAILABILITY IN HADOOP DISTRIBUTED FILE SYSTEM (HDFS) S. Chandra
More informationCS2510 Computer Operating Systems
CS2510 Computer Operating Systems HADOOP Distributed File System Dr. Taieb Znati Computer Science Department University of Pittsburgh Outline HDF Design Issues HDFS Application Profile Block Abstraction
More informationCS2510 Computer Operating Systems
CS2510 Computer Operating Systems HADOOP Distributed File System Dr. Taieb Znati Computer Science Department University of Pittsburgh Outline HDF Design Issues HDFS Application Profile Block Abstraction
More informationGraySort and MinuteSort at Yahoo on Hadoop 0.23
GraySort and at Yahoo on Hadoop.23 Thomas Graves Yahoo! May, 213 The Apache Hadoop[1] software library is an open source framework that allows for the distributed processing of large data sets across clusters
More informationFault 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 informationParallel Programming at the Exascale Era: A Case Study on Parallelizing Matrix Assembly For Unstructured Meshes
Parallel Programming at the Exascale Era: A Case Study on Parallelizing Matrix Assembly For Unstructured Meshes Eric Petit, Loïc Thebault, Quang V. Dinh May 2014 EXA2CT Consortium 2 WPs Organization Proto-Applications
More informationBig Data Technology Map-Reduce Motivation: Indexing in Search Engines
Big Data Technology Map-Reduce Motivation: Indexing in Search Engines Edward Bortnikov & Ronny Lempel Yahoo Labs, Haifa Indexing in Search Engines Information Retrieval s two main stages: Indexing process
More informationA Performance Analysis of Distributed Indexing using Terrier
A Performance Analysis of Distributed Indexing using Terrier Amaury Couste Jakub Kozłowski William Martin Indexing Indexing Used by search
More informationHadoop Distributed File System. Jordan Prosch, Matt Kipps
Hadoop Distributed File System Jordan Prosch, Matt Kipps Outline - Background - Architecture - Comments & Suggestions Background What is HDFS? Part of Apache Hadoop - distributed storage What is Hadoop?
More informationSeeking Opportunities for Hardware Acceleration in Big Data Analytics
Seeking Opportunities for Hardware Acceleration in Big Data Analytics Paul Chow High-Performance Reconfigurable Computing Group Department of Electrical and Computer Engineering University of Toronto Who
More informationChapter 7: Distributed Systems: Warehouse-Scale Computing. Fall 2011 Jussi Kangasharju
Chapter 7: Distributed Systems: Warehouse-Scale Computing Fall 2011 Jussi Kangasharju Chapter Outline Warehouse-scale computing overview Workloads and software infrastructure Failures and repairs Note:
More informationGPU File System Encryption Kartik Kulkarni and Eugene Linkov
GPU File System Encryption Kartik Kulkarni and Eugene Linkov 5/10/2012 SUMMARY. We implemented a file system that encrypts and decrypts files. The implementation uses the AES algorithm computed through
More informationXeon+FPGA Platform for the Data Center
Xeon+FPGA Platform for the Data Center ISCA/CARL 2015 PK Gupta, Director of Cloud Platform Technology, DCG/CPG Overview Data Center and Workloads Xeon+FPGA Accelerator Platform Applications and Eco-system
More informationSurvey on Load Rebalancing for Distributed File System in Cloud
Survey on Load Rebalancing for Distributed File System in Cloud Prof. Pranalini S. Ketkar Ankita Bhimrao Patkure IT Department, DCOER, PG Scholar, Computer Department DCOER, Pune University Pune university
More informationIMPROVED FAIR SCHEDULING ALGORITHM FOR TASKTRACKER IN HADOOP MAP-REDUCE
IMPROVED FAIR SCHEDULING ALGORITHM FOR TASKTRACKER IN HADOOP MAP-REDUCE Mr. Santhosh S 1, Mr. Hemanth Kumar G 2 1 PG Scholor, 2 Asst. Professor, Dept. Of Computer Science & Engg, NMAMIT, (India) ABSTRACT
More informationHadoop 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 informationHybrid Software Architectures for Big Data. Laurence.Hubert@hurence.com @hurence http://www.hurence.com
Hybrid Software Architectures for Big Data Laurence.Hubert@hurence.com @hurence http://www.hurence.com Headquarters : Grenoble Pure player Expert level consulting Training R&D Big Data X-data hot-line
More informationAccelerating and Simplifying Apache
Accelerating and Simplifying Apache Hadoop with Panasas ActiveStor White paper NOvember 2012 1.888.PANASAS www.panasas.com Executive Overview The technology requirements for big data vary significantly
More informationMapReduce 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 informationINTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY
INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK A REVIEW ON HIGH PERFORMANCE DATA STORAGE ARCHITECTURE OF BIGDATA USING HDFS MS.
More informationGraySort on Apache Spark by Databricks
GraySort on Apache Spark by Databricks Reynold Xin, Parviz Deyhim, Ali Ghodsi, Xiangrui Meng, Matei Zaharia Databricks Inc. Apache Spark Sorting in Spark Overview Sorting Within a Partition Range Partitioner
More informationMAPREDUCE Programming Model
CS 2510 COMPUTER OPERATING SYSTEMS Cloud Computing MAPREDUCE Dr. Taieb Znati Computer Science Department University of Pittsburgh MAPREDUCE Programming Model Scaling Data Intensive Application MapReduce
More informationHadoop Cluster Applications
Hadoop Overview Data analytics has become a key element of the business decision process over the last decade. Classic reporting on a dataset stored in a database was sufficient until recently, but yesterday
More informationImproving MapReduce Performance in Heterogeneous Environments
UC Berkeley Improving MapReduce Performance in Heterogeneous Environments Matei Zaharia, Andy Konwinski, Anthony Joseph, Randy Katz, Ion Stoica University of California at Berkeley Motivation 1. MapReduce
More informationA Survey of Cloud Computing Guanfeng Octides
A Survey of Cloud Computing Guanfeng Nov 7, 2010 Abstract The principal service provided by cloud computing is that underlying infrastructure, which often consists of compute resources like storage, processors,
More informationReducer Load Balancing and Lazy Initialization in Map Reduce Environment S.Mohanapriya, P.Natesan
Reducer Load Balancing and Lazy Initialization in Map Reduce Environment S.Mohanapriya, P.Natesan Abstract Big Data is revolutionizing 21st-century with increasingly huge amounts of data to store and be
More informationTake An Internal Look at Hadoop. Hairong Kuang Grid Team, Yahoo! Inc hairong@yahoo-inc.com
Take An Internal Look at Hadoop Hairong Kuang Grid Team, Yahoo! Inc hairong@yahoo-inc.com What s Hadoop Framework for running applications on large clusters of commodity hardware Scale: petabytes of data
More informationBSC vision on Big Data and extreme scale computing
BSC vision on Big Data and extreme scale computing Jesus Labarta, Eduard Ayguade,, Fabrizio Gagliardi, Rosa M. Badia, Toni Cortes, Jordi Torres, Adrian Cristal, Osman Unsal, David Carrera, Yolanda Becerra,
More informationCloud 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 informationMapReduce (in the cloud)
MapReduce (in the cloud) How to painlessly process terabytes of data by Irina Gordei MapReduce Presentation Outline What is MapReduce? Example How it works MapReduce in the cloud Conclusion Demo Motivation:
More informationMapReduce: Simplified Data Processing on Large Clusters. Jeff Dean, Sanjay Ghemawat Google, Inc.
MapReduce: Simplified Data Processing on Large Clusters Jeff Dean, Sanjay Ghemawat Google, Inc. Motivation: Large Scale Data Processing Many tasks: Process lots of data to produce other data Want to use
More informationHadoop Distributed File System. T-111.5550 Seminar On Multimedia 2009-11-11 Eero Kurkela
Hadoop Distributed File System T-111.5550 Seminar On Multimedia 2009-11-11 Eero Kurkela Agenda Introduction Flesh and bones of HDFS Architecture Accessing data Data replication strategy Fault tolerance
More informationMPI and Hybrid Programming Models. William Gropp www.cs.illinois.edu/~wgropp
MPI and Hybrid Programming Models William Gropp www.cs.illinois.edu/~wgropp 2 What is a Hybrid Model? Combination of several parallel programming models in the same program May be mixed in the same source
More informationThe Hadoop Framework
The Hadoop Framework Nils Braden University of Applied Sciences Gießen-Friedberg Wiesenstraße 14 35390 Gießen nils.braden@mni.fh-giessen.de Abstract. The Hadoop Framework offers an approach to large-scale
More informationhttp://www.wordle.net/
Hadoop & MapReduce http://www.wordle.net/ http://www.wordle.net/ Hadoop is an open-source software framework (or platform) for Reliable + Scalable + Distributed Storage/Computational unit Failures completely
More informationUnified Big Data Analytics Pipeline. 连 城 lian@databricks.com
Unified Big Data Analytics Pipeline 连 城 lian@databricks.com What is A fast and general engine for large-scale data processing An open source implementation of Resilient Distributed Datasets (RDD) Has an
More informationHadoop s Entry into the Traditional Analytical DBMS Market. Daniel Abadi Yale University August 3 rd, 2010
Hadoop s Entry into the Traditional Analytical DBMS Market Daniel Abadi Yale University August 3 rd, 2010 Data, Data, Everywhere Data explosion Web 2.0 more user data More devices that sense data More
More informationMapReduce. MapReduce and SQL Injections. CS 3200 Final Lecture. Introduction. MapReduce. Programming Model. Example
MapReduce MapReduce and SQL Injections CS 3200 Final Lecture Jeffrey Dean and Sanjay Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. OSDI'04: Sixth Symposium on Operating System Design
More informationHadoop 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 informationHadoop & its Usage at Facebook
Hadoop & its Usage at Facebook Dhruba Borthakur Project Lead, Hadoop Distributed File System dhruba@apache.org Presented at the The Israeli Association of Grid Technologies July 15, 2009 Outline Architecture
More informationOptimization and analysis of large scale data sorting algorithm based on Hadoop
Optimization and analysis of large scale sorting algorithm based on Hadoop Zhuo Wang, Longlong Tian, Dianjie Guo, Xiaoming Jiang Institute of Information Engineering, Chinese Academy of Sciences {wangzhuo,
More informationLarge scale processing using Hadoop. Ján Vaňo
Large scale processing using Hadoop Ján Vaňo What is Hadoop? Software platform that lets one easily write and run applications that process vast amounts of data Includes: MapReduce offline computing engine
More informationHadoop 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 informationHadoop-BAM and SeqPig
Hadoop-BAM and SeqPig Keijo Heljanko 1, André Schumacher 1,2, Ridvan Döngelci 1, Luca Pireddu 3, Matti Niemenmaa 1, Aleksi Kallio 4, Eija Korpelainen 4, and Gianluigi Zanetti 3 1 Department of Computer
More informationPerformance Analysis and Optimization Tool
Performance Analysis and Optimization Tool Andres S. CHARIF-RUBIAL andres.charif@uvsq.fr Performance Analysis Team, University of Versailles http://www.maqao.org Introduction Performance Analysis Develop
More informationNoSQL Data Base Basics
NoSQL Data Base Basics Course Notes in Transparency Format Cloud Computing MIRI (CLC-MIRI) UPC Master in Innovation & Research in Informatics Spring- 2013 Jordi Torres, UPC - BSC www.jorditorres.eu HDFS
More informationApache 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 informationChapter 18: Database System Architectures. Centralized Systems
Chapter 18: Database System Architectures! Centralized Systems! Client--Server Systems! Parallel Systems! Distributed Systems! Network Types 18.1 Centralized Systems! Run on a single computer system and
More informationApache Ignite TM (Incubating) - In- Memory Data Fabric Fast Data Meets Open Source
Apache Ignite TM (Incubating) - In- Memory Data Fabric Fast Data Meets Open Source DMITRIY SETRAKYAN Founder, PPMC http://www.ignite.incubator.apache.org #apacheignite Agenda Apache Ignite (tm) In- Memory
More informationComparative analysis of mapreduce job by keeping data constant and varying cluster size technique
Comparative analysis of mapreduce job by keeping data constant and varying cluster size technique Mahesh Maurya a, Sunita Mahajan b * a Research Scholar, JJT University, MPSTME, Mumbai, India,maheshkmaurya@yahoo.co.in
More informationHadoop. http://hadoop.apache.org/ Sunday, November 25, 12
Hadoop http://hadoop.apache.org/ What Is Apache Hadoop? The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using
More informationScaling 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 informationmarlabs driving digital agility WHITEPAPER Big Data and Hadoop
marlabs driving digital agility WHITEPAPER Big Data and Hadoop Abstract This paper explains the significance of Hadoop, an emerging yet rapidly growing technology. The prime goal of this paper is to unveil
More information