Big Graph Processing: Some Background

Size: px
Start display at page:

Download "Big Graph Processing: Some Background"

Transcription

1 Big Graph Processing: Some Background Bo Wu Colorado School of Mines Part of slides from: Paul Burkhardt (National Security Agency) and Carlos Guestrin (Washington University) Mines CSCI-580, Bo Wu

2 Graphs are everywhere! o A graph is a collection of binary relationships, i.e. networks of pairwise interactions including social networks, digital networks part of Internet Mines CSCI-580, Bo Wu brain network 2

3 Scale of the first graph o Nearly 300 years ago the first graph problem consisted of 4 vertices and 7 edges Seven Bridges of Konigsberg problem Is it possible to cross each of the seven bridges exactly once? Not too hard to fit in memory Mines CSCI-580, Bo Wu 3

4 Scale of real-world graphs o Graph scale in current CS literature on order of billions of edges, tens of gigabytes Mines CSCI-580, Bo Wu 4

5 Big Data begets Big Graphs o Increasing volume,velocity,variety of Big Data are significant challenges to scalable algorithms o How will graph applications adapt to Big Data at petabyte scale? o Ability to store and process Big Graphs impacts typical data structures Mines CSCI-580, Bo Wu 5

6 Social Scale o 1 billion vertices, 100 billion edges 111 PB adjacency matrix 2.92 TB adjacency list 2.92 TB edge list Mines CSCI-580, Bo Wu 6

7 Web scale o 50 billion vertices, 1 trillion edges 271 EB adjacency matrix 29.5 TB adjacency list 29.1 TB edge list Mines CSCI-580, Bo Wu 7

8 Brain scale o 100 billion vertices, 100 trillion edge 2.84 PB adjacency list 2.84 PB edge list Mines CSCI-580, Bo Wu 8

9 Benchmarking scalability on Big Graphs o Big Graph challenge our conventional thinking on both algorithms and computer architecture! o New Graph500.org benchmark provides a foundation for conducting experiments on graph datasets Mines CSCI-580, Bo Wu 9

10 Graph algorithms are challenging o Difficult to parallelize irregular data accesses increase latency skewed data distribution creates bottlenecks Celebrity nodes in social networks o Increased size imposes greater storage overhead IO burden! Mines CSCI-580, Bo Wu 10

11 Problem: How do we store and process Big Graphs? o Conventional approach is to store and compute inmemory o Shared memory Parallel Random Access Machine (PRAM) data in globally-shared memory implicit communication by updating memory fast-random access o Distributed memory Bulk Synchronous Parallel (BSP) data distributed to local, private memory explicit communication by sending messages easier to scale by adding more machines Mines CSCI-580, Bo Wu 11

12 Memory is fast but o Algorithms must exploit computer memory hierarchy designed for spatial and temporal locality registers, L1,L2,L3 cache, TLB, pages, disk... great for unit-stride access common in many scientific codes, e.g. linear algebra o But common graph algorithm implementations have... lots of random access to memory causing... many cache and TLB misses Mines CSCI-580, Bo Wu 12

13 Poor locality increases latency o Question: What is the memory throughput if 90% TLB hit and 0.01% page fault on miss? Mines CSCI-580, Bo Wu 13

14 If it fits o Graph problems that fit in memory can leverage excellent advances in architecture and libraries... Cray XMT2 designed for latency-hiding SGI UV2 designed for large, cache-coherent shared-memory body of literature and libraries Parallel Boost Graph Library (PBGL) Indiana University Multithreaded Graph Library (MTGL) Sandia National Labs GraphCT/STINGER Georgia Tech GraphLab Carnegie Mellon University Giraph Apache Software Foundation Mines CSCI-580, Bo Wu 14

15 We can add more memory, but o Memory capacity is limited by... number of CPU pins, memory controller channels, DIMMs per channel memory bus width o Globally-shared memory limited by... CPU address space cache-coherence Mines CSCI-580, Bo Wu 15

16 Larger systems, greater latency o Increasing memory can increase latency traverse more memory addresses larger system with greater physical distance between machines Fundamental limitation: speed of light o Latency causes significant inefficiency in new CPU architectures Mines CSCI-580, Bo Wu 16

17 Easier to increase capacity using disks o Current Intel Xeon E5 architectures: 384 GB max. per CPU (4 channels x 3 DIMMS x 32 GB) 64 TB max. globally-shared memory (46-bit address space) 3881 dual Xeon E5 motherboards to store Brain Graph 98 racks o Disk capacity not unlimited but higher than memory Larget disk on market: 8TB needs 364 to store Brain Graph which can fit in 5 racks o Disk is not enough applications will still require memory for processing Mines CSCI-580, Bo Wu 17

18 Big Graph Processing Frameworks

19 Why not just map reduce? o Developed by Google o Excellent for embarrassingly massively parallel computations No communication needed Many machine learning algorithms fall into this category o Not efficient for iterative algorithms that have dependences Unnecessary IO traffic 19

20 What s the natural way to program graph computation? 20

21 Most famous parallel graph processing framework 21

22 GAS 22

23 We still need parallelism 23

24 Graph partition: not easy at all at scale 24

25 Power-law distribution count More$than$10 8 $ver+ces$$ have$one$neighbor.$ Top$1%$of$ver+ces$are$ High%Degree)) adjacent$to$ Ver+ces) 50%$of$the$edges!$ degree 25

26 Power-law degree distribution 26

27 Random graph partitioning o Graph parallel abstractions rely on partitioning: Minimize communication Balance computation and storage 10 Machines à 90% of edges cut 100 Machines à 99% of edges cut! Machine 1 Machine 2 27

28 Challenges of high-degree vertices Y Data transmitted across network O(# cut edges) Machine 1 Machine 2 28

29 Idea of vertex cut 29

30 GAS decomposition 30

31 Random Edge- Placement Randomly assign edges to machines Machine 1 Machine 2 Machine 3 Balanced Vertex- Cut Y Spans 3 Machines Z Spans 2 Machines Not cut! YY Y Z 31

32 Greedy Vertex- Cuts Place edges on machines which already have the vertices in that edge. A B B C Machine1 Machine 2 AB DE 32

33 Example What s the popularity of this user? Popular?) 33

34 PargeRank Algorithm R[i] = Rank%of% user%i" X j2nbrs(i) w ji R[j] Weighted%sum%of% neighbors %ranks" o Update ranks in parallel o Iterate until convergence 34

35 PageRank in Graphlab GraphLab_PageRank(i) // Compute sum over neighbors total = 0 foreach( j in in_neighbors(i)): total = total + R[j] * w ji! // Update the PageRank R[i] = total! // Trigger neighbors to run again if R[i] not converged then foreach( j in out_neighbors(i)) signal vertex- program on j Gather Information About Neighborhood Update Vertex Signal Neighbors & Modify Edge Data 35

36 Triangle counting on Twitter 36

37 What if I don t have a cluster? 37

38 GraphChi disk-based GraphLab o Challenge Random disk accesses! o Naive solutions Graph clustering Prefetching! o Solution Novel graph representation in disk Parallel sliding window Minimizes random accesses 38

39 Parallel sliding window layout A shard is easy to fit in memory 39

40 Parallel sliding window execution O(P^2) random accesses per pass on entire graph 40

41 Triangle counting on Twitter graph 41

An NSA Big Graph experiment. Paul Burkhardt, Chris Waring. May 20, 2013

An NSA Big Graph experiment. Paul Burkhardt, Chris Waring. May 20, 2013 U.S. National Security Agency Research Directorate - R6 Technical Report NSA-RD-2013-056002v1 May 20, 2013 Graphs are everywhere! A graph is a collection of binary relationships, i.e. networks of pairwise

More information

Asking Hard Graph Questions. Paul Burkhardt. February 3, 2014

Asking Hard Graph Questions. Paul Burkhardt. February 3, 2014 Beyond Watson: Predictive Analytics and Big Data U.S. National Security Agency Research Directorate - R6 Technical Report February 3, 2014 300 years before Watson there was Euler! The first (Jeopardy!)

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

Fast Iterative Graph Computation with Resource Aware Graph Parallel Abstraction

Fast Iterative Graph Computation with Resource Aware Graph Parallel Abstraction Human connectome. Gerhard et al., Frontiers in Neuroinformatics 5(3), 2011 2 NA = 6.022 1023 mol 1 Paul Burkhardt, Chris Waring An NSA Big Graph experiment Fast Iterative Graph Computation with Resource

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

A Performance Evaluation of Open Source Graph Databases. Robert McColl David Ediger Jason Poovey Dan Campbell David A. Bader

A Performance Evaluation of Open Source Graph Databases. Robert McColl David Ediger Jason Poovey Dan Campbell David A. Bader A Performance Evaluation of Open Source Graph Databases Robert McColl David Ediger Jason Poovey Dan Campbell David A. Bader Overview Motivation Options Evaluation Results Lessons Learned Moving Forward

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

Trinity: A Distributed Graph Engine on a Memory Cloud

Trinity: A Distributed Graph Engine on a Memory Cloud Trinity: A Distributed Graph Engine on a Memory Cloud Review of: B. Shao, H. Wang, Y. Li. Trinity: a distributed graph engine on a memory cloud, Proc. ACM SIGMOD International Conference on Management

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

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

Massive Streaming Data Analytics: A Case Study with Clustering Coefficients. David Ediger, Karl Jiang, Jason Riedy and David A.

Massive Streaming Data Analytics: A Case Study with Clustering Coefficients. David Ediger, Karl Jiang, Jason Riedy and David A. Massive Streaming Data Analytics: A Case Study with Clustering Coefficients David Ediger, Karl Jiang, Jason Riedy and David A. Bader Overview Motivation A Framework for Massive Streaming hello Data Analytics

More information

MapReduce Algorithms. Sergei Vassilvitskii. Saturday, August 25, 12

MapReduce Algorithms. Sergei Vassilvitskii. Saturday, August 25, 12 MapReduce Algorithms A Sense of Scale At web scales... Mail: Billions of messages per day Search: Billions of searches per day Social: Billions of relationships 2 A Sense of Scale At web scales... Mail:

More information

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

Four Orders of Magnitude: Running Large Scale Accumulo Clusters. Aaron Cordova Accumulo Summit, June 2014

Four Orders of Magnitude: Running Large Scale Accumulo Clusters. Aaron Cordova Accumulo Summit, June 2014 Four Orders of Magnitude: Running Large Scale Accumulo Clusters Aaron Cordova Accumulo Summit, June 2014 Scale, Security, Schema Scale to scale 1 - (vt) to change the size of something let s scale the

More information

Binary search tree with SIMD bandwidth optimization using SSE

Binary 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 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

FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs

FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs Da Zheng, Disa Mhembere, Randal Burns, Joshua Vogelstein, Carey E. Priebe, and Alexander S. Szalay, Johns Hopkins University https://www.usenix.org/conference/fast5/technical-sessions/presentation/zheng

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 Big Data Analytics Big Data Analytics 1 / 33 Outline

More information

Achieving Real-Time Business Solutions Using Graph Database Technology and High Performance Networks

Achieving Real-Time Business Solutions Using Graph Database Technology and High Performance Networks WHITE PAPER July 2014 Achieving Real-Time Business Solutions Using Graph Database Technology and High Performance Networks Contents Executive Summary...2 Background...3 InfiniteGraph...3 High Performance

More information

SYSTAP / bigdata. Open Source High Performance Highly Available. 1 http://www.bigdata.com/blog. bigdata Presented to CSHALS 2/27/2014

SYSTAP / bigdata. Open Source High Performance Highly Available. 1 http://www.bigdata.com/blog. bigdata Presented to CSHALS 2/27/2014 SYSTAP / Open Source High Performance Highly Available 1 SYSTAP, LLC Small Business, Founded 2006 100% Employee Owned Customers OEMs and VARs Government TelecommunicaHons Health Care Network Storage Finance

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

Big Data Systems CS 5965/6965 FALL 2015

Big Data Systems CS 5965/6965 FALL 2015 Big Data Systems CS 5965/6965 FALL 2015 Today General course overview Expectations from this course Q&A Introduction to Big Data Assignment #1 General Course Information Course Web Page http://www.cs.utah.edu/~hari/teaching/fall2015.html

More information

Using an In-Memory Data Grid for Near Real-Time Data Analysis

Using an In-Memory Data Grid for Near Real-Time Data Analysis SCALEOUT SOFTWARE Using an In-Memory Data Grid for Near Real-Time Data Analysis by Dr. William Bain, ScaleOut Software, Inc. 2012 ScaleOut Software, Inc. 12/27/2012 IN today s competitive world, businesses

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

RevoScaleR Speed and Scalability

RevoScaleR Speed and Scalability EXECUTIVE WHITE PAPER RevoScaleR Speed and Scalability By Lee Edlefsen Ph.D., Chief Scientist, Revolution Analytics Abstract RevoScaleR, the Big Data predictive analytics library included with Revolution

More information

STINGER: High Performance Data Structure for Streaming Graphs

STINGER: High Performance Data Structure for Streaming Graphs STINGER: High Performance Data Structure for Streaming Graphs David Ediger Rob McColl Jason Riedy David A. Bader Georgia Institute of Technology Atlanta, GA, USA Abstract The current research focus on

More information

Graph Processing and Social Networks

Graph Processing and Social Networks Graph Processing and Social Networks Presented by Shu Jiayu, Yang Ji Department of Computer Science and Engineering The Hong Kong University of Science and Technology 2015/4/20 1 Outline Background Graph

More information

Efficient Parallel Graph Exploration on Multi-Core CPU and GPU

Efficient Parallel Graph Exploration on Multi-Core CPU and GPU Efficient Parallel Graph Exploration on Multi-Core CPU and GPU Pervasive Parallelism Laboratory Stanford University Sungpack Hong, Tayo Oguntebi, and Kunle Olukotun Graph and its Applications Graph Fundamental

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

Presto/Blockus: Towards Scalable R Data Analysis

Presto/Blockus: Towards Scalable R Data Analysis /Blockus: Towards Scalable R Data Analysis Andrew A. Chien University of Chicago and Argonne ational Laboratory IRIA-UIUC-AL Joint Institute Potential Collaboration ovember 19, 2012 ovember 19, 2012 Andrew

More information

Systems and Algorithms for Big Data Analytics

Systems and Algorithms for Big Data Analytics Systems and Algorithms for Big Data Analytics YAN, Da Email: yanda@cse.cuhk.edu.hk My Research Graph Data Distributed Graph Processing Spatial Data Spatial Query Processing Uncertain Data Querying & Mining

More information

Cray: Enabling Real-Time Discovery in Big Data

Cray: Enabling Real-Time Discovery in Big Data Cray: Enabling Real-Time Discovery in Big Data Discovery is the process of gaining valuable insights into the world around us by recognizing previously unknown relationships between occurrences, objects

More information

Graph Database Proof of Concept Report

Graph Database Proof of Concept Report Objectivity, Inc. Graph Database Proof of Concept Report Managing The Internet of Things Table of Contents Executive Summary 3 Background 3 Proof of Concept 4 Dataset 4 Process 4 Query Catalog 4 Environment

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

September 25, 2007. Maya Gokhale Georgia Institute of Technology

September 25, 2007. Maya Gokhale Georgia Institute of Technology NAND Flash Storage for High Performance Computing Craig Ulmer cdulmer@sandia.gov September 25, 2007 Craig Ulmer Maya Gokhale Greg Diamos Michael Rewak SNL/CA, LLNL Georgia Institute of Technology University

More information

2009 Oracle Corporation 1

2009 Oracle Corporation 1 The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material,

More information

B669 Sublinear Algorithms for Big Data

B669 Sublinear Algorithms for Big Data B669 Sublinear Algorithms for Big Data Qin Zhang 1-1 Now about the Big Data Big data is everywhere : over 2.5 petabytes of sales transactions : an index of over 19 billion web pages : over 40 billion of

More information

Introduction to Multiprocessors (Part I) Prof. Cristina Silvano Politecnico di Milano

Introduction to Multiprocessors (Part I) Prof. Cristina Silvano Politecnico di Milano Introduction to Multiprocessors (Part I) Prof. Cristina Silvano Politecnico di Milano Outline Key issues to design multiprocessors Interconnection network Centralized shared-memory architectures Distributed

More information

Understanding Neo4j Scalability

Understanding Neo4j Scalability Understanding Neo4j Scalability David Montag January 2013 Understanding Neo4j Scalability Scalability means different things to different people. Common traits associated include: 1. Redundancy in the

More information

Extreme Computing. Big Data. Stratis Viglas. School of Informatics University of Edinburgh sviglas@inf.ed.ac.uk. Stratis Viglas Extreme Computing 1

Extreme Computing. Big Data. Stratis Viglas. School of Informatics University of Edinburgh sviglas@inf.ed.ac.uk. Stratis Viglas Extreme Computing 1 Extreme Computing Big Data Stratis Viglas School of Informatics University of Edinburgh sviglas@inf.ed.ac.uk Stratis Viglas Extreme Computing 1 Petabyte Age Big Data Challenges Stratis Viglas Extreme Computing

More information

BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB

BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB Planet Size Data!? Gartner s 10 key IT trends for 2012 unstructured data will grow some 80% over the course of the next

More information

References. Slides are heavily based on, and contain content from: Technology in Action Eleventh Edition by Evans, Martin, and Poatsy

References. Slides are heavily based on, and contain content from: Technology in Action Eleventh Edition by Evans, Martin, and Poatsy Hardware CSCI 101 References Slides are heavily based on, and contain content from: Technology in Action Eleventh Edition by Evans, Martin, and Poatsy Computer Functions Input Process Store Output Computer

More information

Distributed communication-aware load balancing with TreeMatch in Charm++

Distributed communication-aware load balancing with TreeMatch in Charm++ Distributed communication-aware load balancing with TreeMatch in Charm++ The 9th Scheduling for Large Scale Systems Workshop, Lyon, France Emmanuel Jeannot Guillaume Mercier Francois Tessier In collaboration

More information

SIGMOD RWE Review Towards Proximity Pattern Mining in Large Graphs

SIGMOD RWE Review Towards Proximity Pattern Mining in Large Graphs SIGMOD RWE Review Towards Proximity Pattern Mining in Large Graphs Fabian Hueske, TU Berlin June 26, 21 1 Review This document is a review report on the paper Towards Proximity Pattern Mining in Large

More information

The Power of Relationships

The Power of Relationships The Power of Relationships Opportunities and Challenges in Big Data Intel Labs Cluster Computing Architecture Legal Notices INFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO

More information

A1 and FARM scalable graph database on top of a transactional memory layer

A1 and FARM scalable graph database on top of a transactional memory layer A1 and FARM scalable graph database on top of a transactional memory layer Miguel Castro, Aleksandar Dragojević, Dushyanth Narayanan, Ed Nightingale, Alex Shamis Richie Khanna, Matt Renzelmann Chiranjeeb

More information

DATA ANALYSIS II. Matrix Algorithms

DATA ANALYSIS II. Matrix Algorithms DATA ANALYSIS II Matrix Algorithms Similarity Matrix Given a dataset D = {x i }, i=1,..,n consisting of n points in R d, let A denote the n n symmetric similarity matrix between the points, given as where

More information

High Performance Computing. Course Notes 2007-2008. HPC Fundamentals

High Performance Computing. Course Notes 2007-2008. HPC Fundamentals High Performance Computing Course Notes 2007-2008 2008 HPC Fundamentals Introduction What is High Performance Computing (HPC)? Difficult to define - it s a moving target. Later 1980s, a supercomputer performs

More information

Scalable Data Analysis in R. Lee E. Edlefsen Chief Scientist UserR! 2011

Scalable Data Analysis in R. Lee E. Edlefsen Chief Scientist UserR! 2011 Scalable Data Analysis in R Lee E. Edlefsen Chief Scientist UserR! 2011 1 Introduction Our ability to collect and store data has rapidly been outpacing our ability to analyze it We need scalable data analysis

More information

JVM Performance Study Comparing Oracle HotSpot and Azul Zing Using Apache Cassandra

JVM Performance Study Comparing Oracle HotSpot and Azul Zing Using Apache Cassandra JVM Performance Study Comparing Oracle HotSpot and Azul Zing Using Apache Cassandra January 2014 Legal Notices Apache Cassandra, Spark and Solr and their respective logos are trademarks or registered trademarks

More information

Outline. Motivation. Motivation. MapReduce & GraphLab: Programming Models for Large-Scale Parallel/Distributed Computing 2/28/2013

Outline. Motivation. Motivation. MapReduce & GraphLab: Programming Models for Large-Scale Parallel/Distributed Computing 2/28/2013 MapReduce & GraphLab: Programming Models for Large-Scale Parallel/Distributed Computing Iftekhar Naim Outline Motivation MapReduce Overview Design Issues & Abstractions Examples and Results Pros and Cons

More information

Chapter 18: Database System Architectures. Centralized Systems

Chapter 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 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

bigdata Managing Scale in Ontological Systems

bigdata Managing Scale in Ontological Systems Managing Scale in Ontological Systems 1 This presentation offers a brief look scale in ontological (semantic) systems, tradeoffs in expressivity and data scale, and both information and systems architectural

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

Tableau Server 7.0 scalability

Tableau Server 7.0 scalability Tableau Server 7.0 scalability February 2012 p2 Executive summary In January 2012, we performed scalability tests on Tableau Server to help our customers plan for large deployments. We tested three different

More information

Data Centric Systems (DCS)

Data Centric Systems (DCS) Data Centric Systems (DCS) Architecture and Solutions for High Performance Computing, Big Data and High Performance Analytics High Performance Computing with Data Centric Systems 1 Data Centric Systems

More information

Measuring Cache and Memory Latency and CPU to Memory Bandwidth

Measuring Cache and Memory Latency and CPU to Memory Bandwidth White Paper Joshua Ruggiero Computer Systems Engineer Intel Corporation Measuring Cache and Memory Latency and CPU to Memory Bandwidth For use with Intel Architecture December 2008 1 321074 Executive Summary

More information

FPGA-based Multithreading for In-Memory Hash Joins

FPGA-based Multithreading for In-Memory Hash Joins FPGA-based Multithreading for In-Memory Hash Joins Robert J. Halstead, Ildar Absalyamov, Walid A. Najjar, Vassilis J. Tsotras University of California, Riverside Outline Background What are FPGAs Multithreaded

More information

Multi-Threading Performance on Commodity Multi-Core Processors

Multi-Threading Performance on Commodity Multi-Core Processors Multi-Threading Performance on Commodity Multi-Core Processors Jie Chen and William Watson III Scientific Computing Group Jefferson Lab 12000 Jefferson Ave. Newport News, VA 23606 Organization Introduction

More information

Symmetric Multiprocessing

Symmetric Multiprocessing Multicore Computing A multi-core processor is a processing system composed of two or more independent cores. One can describe it as an integrated circuit to which two or more individual processors (called

More information

Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics

Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics Juwei Shi, Yunjie Qiu, Umar Farooq Minhas, Limei Jiao, Chen Wang, Berthold Reinwald, and Fatma Özcan IBM Research China IBM Almaden

More information

Enterprise Applications

Enterprise Applications Enterprise Applications Chi Ho Yue Sorav Bansal Shivnath Babu Amin Firoozshahian EE392C Emerging Applications Study Spring 2003 Functionality Online Transaction Processing (OLTP) Users/apps interacting

More information

Common Patterns and Pitfalls for Implementing Algorithms in Spark. Hossein Falaki @mhfalaki hossein@databricks.com

Common Patterns and Pitfalls for Implementing Algorithms in Spark. Hossein Falaki @mhfalaki hossein@databricks.com Common Patterns and Pitfalls for Implementing Algorithms in Spark Hossein Falaki @mhfalaki hossein@databricks.com Challenges of numerical computation over big data When applying any algorithm to big data

More information

HADOOP ON ORACLE ZFS STORAGE A TECHNICAL OVERVIEW

HADOOP ON ORACLE ZFS STORAGE A TECHNICAL OVERVIEW HADOOP ON ORACLE ZFS STORAGE A TECHNICAL OVERVIEW 757 Maleta Lane, Suite 201 Castle Rock, CO 80108 Brett Weninger, Managing Director brett.weninger@adurant.com Dave Smelker, Managing Principal dave.smelker@adurant.com

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 In-Memory Computing to Simplify Big Data Analytics

Using In-Memory Computing to Simplify Big Data Analytics SCALEOUT SOFTWARE Using In-Memory Computing to Simplify Big Data Analytics by Dr. William Bain, ScaleOut Software, Inc. 2012 ScaleOut Software, Inc. 12/27/2012 T he big data revolution is upon us, fed

More information

NoSQL Performance Test In-Memory Performance Comparison of SequoiaDB, Cassandra, and MongoDB

NoSQL Performance Test In-Memory Performance Comparison of SequoiaDB, Cassandra, and MongoDB bankmark UG (haftungsbeschränkt) Bahnhofstraße 1 9432 Passau Germany www.bankmark.de info@bankmark.de T +49 851 25 49 49 F +49 851 25 49 499 NoSQL Performance Test In-Memory Performance Comparison of SequoiaDB,

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

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

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

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

In-Memory Databases Algorithms and Data Structures on Modern Hardware. Martin Faust David Schwalb Jens Krüger Jürgen Müller

In-Memory Databases Algorithms and Data Structures on Modern Hardware. Martin Faust David Schwalb Jens Krüger Jürgen Müller In-Memory Databases Algorithms and Data Structures on Modern Hardware Martin Faust David Schwalb Jens Krüger Jürgen Müller The Free Lunch Is Over 2 Number of transistors per CPU increases Clock frequency

More information

Comparing SMB Direct 3.0 performance over RoCE, InfiniBand and Ethernet. September 2014

Comparing SMB Direct 3.0 performance over RoCE, InfiniBand and Ethernet. September 2014 Comparing SMB Direct 3.0 performance over RoCE, InfiniBand and Ethernet Anand Rangaswamy September 2014 Storage Developer Conference Mellanox Overview Ticker: MLNX Leading provider of high-throughput,

More information

Increasing Flash Throughput for Big Data Applications (Data Management Track)

Increasing Flash Throughput for Big Data Applications (Data Management Track) Scale Simplify Optimize Evolve Increasing Flash Throughput for Big Data Applications (Data Management Track) Flash Memory 1 Industry Context Addressing the challenge A proposed solution Review of the Benefits

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

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

Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science IBM Chief Scientist, Graph Computing. October 29th, 2015

Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science IBM Chief Scientist, Graph Computing. October 29th, 2015 E6893 Big Data Analytics Lecture 8: Spark Streams and Graph Computing (I) Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science IBM Chief Scientist, Graph Computing

More information

FRIEDRICH-ALEXANDER-UNIVERSITÄT ERLANGEN-NÜRNBERG

FRIEDRICH-ALEXANDER-UNIVERSITÄT ERLANGEN-NÜRNBERG FRIEDRICH-ALEXANDER-UNIVERSITÄT ERLANGEN-NÜRNBERG INSTITUT FÜR INFORMATIK (MATHEMATISCHE MASCHINEN UND DATENVERARBEITUNG) Lehrstuhl für Informatik 10 (Systemsimulation) Massively Parallel Multilevel Finite

More information

Distance Degree Sequences for Network Analysis

Distance Degree Sequences for Network Analysis Universität Konstanz Computer & Information Science Algorithmics Group 15 Mar 2005 based on Palmer, Gibbons, and Faloutsos: ANF A Fast and Scalable Tool for Data Mining in Massive Graphs, SIGKDD 02. Motivation

More information

Diablo and VMware TM powering SQL Server TM in Virtual SAN TM. A Diablo Technologies Whitepaper. May 2015

Diablo and VMware TM powering SQL Server TM in Virtual SAN TM. A Diablo Technologies Whitepaper. May 2015 A Diablo Technologies Whitepaper Diablo and VMware TM powering SQL Server TM in Virtual SAN TM May 2015 Ricky Trigalo, Director for Virtualization Solutions Architecture, Diablo Technologies Daniel Beveridge,

More information

An Oracle White Paper June 2012. High Performance Connectors for Load and Access of Data from Hadoop to Oracle Database

An Oracle White Paper June 2012. High Performance Connectors for Load and Access of Data from Hadoop to Oracle Database An Oracle White Paper June 2012 High Performance Connectors for Load and Access of Data from Hadoop to Oracle Database Executive Overview... 1 Introduction... 1 Oracle Loader for Hadoop... 2 Oracle Direct

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

Parallel Simplification of Large Meshes on PC Clusters

Parallel Simplification of Large Meshes on PC Clusters Parallel Simplification of Large Meshes on PC Clusters Hua Xiong, Xiaohong Jiang, Yaping Zhang, Jiaoying Shi State Key Lab of CAD&CG, College of Computer Science Zhejiang University Hangzhou, China April

More information

White Paper. Recording Server Virtualization

White Paper. Recording Server Virtualization White Paper Recording Server Virtualization Prepared by: Mike Sherwood, Senior Solutions Engineer Milestone Systems 23 March 2011 Table of Contents Introduction... 3 Target audience and white paper purpose...

More information

The Sierra Clustered Database Engine, the technology at the heart of

The Sierra Clustered Database Engine, the technology at the heart of A New Approach: Clustrix Sierra Database Engine The Sierra Clustered Database Engine, the technology at the heart of the Clustrix solution, is a shared-nothing environment that includes the Sierra Parallel

More information

High Performance Spatial Queries and Analytics for Spatial Big Data. Fusheng Wang. Department of Biomedical Informatics Emory University

High Performance Spatial Queries and Analytics for Spatial Big Data. Fusheng Wang. Department of Biomedical Informatics Emory University High Performance Spatial Queries and Analytics for Spatial Big Data Fusheng Wang Department of Biomedical Informatics Emory University Introduction Spatial Big Data Geo-crowdsourcing:OpenStreetMap Remote

More information

SMB Direct for SQL Server and Private Cloud

SMB Direct for SQL Server and Private Cloud SMB Direct for SQL Server and Private Cloud Increased Performance, Higher Scalability and Extreme Resiliency June, 2014 Mellanox Overview Ticker: MLNX Leading provider of high-throughput, low-latency server

More information

Surfing the Data Tsunami: A New Paradigm for Big Data Processing and Analytics

Surfing the Data Tsunami: A New Paradigm for Big Data Processing and Analytics Surfing the Data Tsunami: A New Paradigm for Big Data Processing and Analytics Dr. Liangxiu Han Future Networks and Distributed Systems Group (FUNDS) School of Computing, Mathematics and Digital Technology,

More information

Parallel Algorithms for Small-world Network. David A. Bader and Kamesh Madduri

Parallel Algorithms for Small-world Network. David A. Bader and Kamesh Madduri Parallel Algorithms for Small-world Network Analysis ayssand Partitioning atto g(s (SNAP) David A. Bader and Kamesh Madduri Overview Informatics networks, small-world topology Community Identification/Graph

More information

Big Data With Hadoop

Big 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 information

Distributed Optimization of Fiber Optic Network Layout using MATLAB. R. Pfarrhofer, M. Kelz, P. Bachhiesl, H. Stögner, and A. Uhl

Distributed Optimization of Fiber Optic Network Layout using MATLAB. R. Pfarrhofer, M. Kelz, P. Bachhiesl, H. Stögner, and A. Uhl Distributed Optimization of Fiber Optic Network Layout using MATLAB R. Pfarrhofer, M. Kelz, P. Bachhiesl, H. Stögner, and A. Uhl uhl@cosy.sbg.ac.at R. Pfarrhofer, M. Kelz, P. Bachhiesl, H. Stögner, and

More information

Big Data on AWS. Services Overview. Bernie Nallamotu Principle Solutions Architect

Big Data on AWS. Services Overview. Bernie Nallamotu Principle Solutions Architect on AWS Services Overview Bernie Nallamotu Principle Solutions Architect \ So what is it? When your data sets become so large that you have to start innovating around how to collect, store, organize, analyze

More information

Business white paper. HP Process Automation. Version 7.0. Server performance

Business white paper. HP Process Automation. Version 7.0. Server performance Business white paper HP Process Automation Version 7.0 Server performance Table of contents 3 Summary of results 4 Benchmark profile 5 Benchmark environmant 6 Performance metrics 6 Process throughput 6

More information

Performance Evaluations of Graph Database using CUDA and OpenMP Compatible Libraries

Performance Evaluations of Graph Database using CUDA and OpenMP Compatible Libraries Performance Evaluations of Graph Database using CUDA and OpenMP Compatible Libraries Shin Morishima 1 and Hiroki Matsutani 1,2,3 1Keio University, 3 14 1 Hiyoshi, Kohoku ku, Yokohama, Japan 2National Institute

More information

MapReduce and Hadoop. Aaron Birkland Cornell Center for Advanced Computing. January 2012

MapReduce and Hadoop. Aaron Birkland Cornell Center for Advanced Computing. January 2012 MapReduce and Hadoop Aaron Birkland Cornell Center for Advanced Computing January 2012 Motivation Simple programming model for Big Data Distributed, parallel but hides this Established success at petabyte

More information

Overlapping Data Transfer With Application Execution on Clusters

Overlapping Data Transfer With Application Execution on Clusters Overlapping Data Transfer With Application Execution on Clusters Karen L. Reid and Michael Stumm reid@cs.toronto.edu stumm@eecg.toronto.edu Department of Computer Science Department of Electrical and Computer

More information

Datacenter Operating Systems

Datacenter Operating Systems Datacenter Operating Systems CSE451 Simon Peter With thanks to Timothy Roscoe (ETH Zurich) Autumn 2015 This Lecture What s a datacenter Why datacenters Types of datacenters Hyperscale datacenters Major

More information

Distributed R for Big Data

Distributed R for Big Data Distributed R for Big Data Indrajit Roy, HP Labs November 2013 Team: Shivara m Erik Kyungyon g Alvin Rob Vanish A Big Data story Once upon a time, a customer in distress had. 2+ billion rows of financial

More information

Complexity and Scalability in Semantic Graph Analysis Semantic Days 2013

Complexity and Scalability in Semantic Graph Analysis Semantic Days 2013 Complexity and Scalability in Semantic Graph Analysis Semantic Days 2013 James Maltby, Ph.D 1 Outline of Presentation Semantic Graph Analytics Database Architectures In-memory Semantic Database Formulation

More information