Big Data and High Performance Computing
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This document discusses big data and high performance computing. It begins by outlining where big data comes from, including sources like people, organizations, and machines. It then discusses opportunities that can be derived from big data analysis. The document explains the "big data problem" of how to process and store massive amounts of data across clusters. It provides background on why distributed computing solutions are needed now given exponential growth in digital data. The Hadoop ecosystem is introduced as a big data technology stack. The document outlines MapReduce and HDFS as core distributed computing architectures. It also discusses GPUs and massive parallelization using CUDA to enable high performance computing for big data workloads.
![MapReduce Architecture
INPUTDATA
OUTPUTDATA
Map()
Map()
Map()
Map()
Reduce()
Reduce()
Split
[k1, v1]
Sort
by k
Merge
[k1, [v1, v2, …, vN]]](https://image.slidesharecdn.com/amcham-big-data-hpc-200512-low-200513184135/85/Big-Data-and-High-Performance-Computing-29-320.jpg)
Big Data and High Performance Computing
- 1. Big Data and High Performance Computing Dr. Abzetdin ADAMOV Center for Data Analytics Research (CeDAR) School of IT & Engineering ADA University aadamov@ada.edu.az
- 2. Speech Content • Where Big Data Comes From • Opportunities derived from Big Data • Understanding Big Data Problem • Why Now? • Hadoop Ecosystem • Big Data Computing Solution • Massive Parallelization • Q&A
- 3. WHERE BIG DATA COMES FROM
- 4. Where we were? AAdamov, CeDAR, ADA University Pope Benedict inauguration in 2005
- 5. Where we are? AAdamov, CeDAR, ADA University Pope Francis inauguration in 2013
- 6. Digital Universe Volume • 2003 – 5 exabytes from beginning of civilization • 2005 – 130 exabytes • 2008 – 480.000 petabytes (PB) • 2009 – 800.000 PB • 2010 – 1200 000 PB or 1.2 zettabyte (ZB) • 2012 – 2.7 ZB • 2014 ~ 6.2 ZB • 2015 ~ 10 ZB • 2017 ~ 16 ZB • 2019 ~ 30 ZB • 2020 estimated 44 ZB Every day now we create as much information as we did from the dawn of civilization up until 2003 CeDAWI Research Center
- 7. Where Data Comes From Data is produced by: • People • Social Media, Public Web, Smartphones, … • Organizations (Employer) • OLTP, OLAP, BI, … • Machines • IoT, Satellites, Vehicles, Science, … CeDAWI Research Center
- 8. Modern Data Sources à User Generated Content (Web & Mobile) • Twitter, Facebook, Snapchat, YouTube • Clickstream, Ads, User Engagement • Payments: Paypal, Venmo à Internet of Anything (IoAT) • Wind Turbines, Oil Rigs, Cars • Weather Stations, Smart Grids • RFID Tags, Beacons, Wearables
- 9. Data Variety – Multiple Formats • Structured: 5-10% of all Data Universe • SQL - Databases • Semi-Structured: 5-10% • CSV, XML, JSON, email structure • Unstructured: 80-90% • books, journals, documents, metadata, log files, health records, audio, video, images, files, email message, Web page, social media, word- processor document, ...
- 10. OPPORTUNITIES FROM BIG DATA
- 11. Recommenda- tion engines Smart meter monitoring Equipment monitoring Advertising analysis Life sciences research Fraud detection Healthcare outcomes Weather forecasting for business planning Oil & Gas exploration Social network analysis Churn analysis Traffic flow optimization IT infrastructure & Web App optimization Legal discovery and document archiving Data Analytics is needed everywhere Intelligence Gathering Location-based tracking & services Pricing Analysis Personalized Insurance
- 12. But do you have the capacity to refine it? DATA is the NEW OIL! AAdamov, CeDAR, ADA University
- 13. UNDERSTANDING BIG DATA PROBLEM
- 14. The “Big Data” Problem à A single machine cannot process or even store all the data! Problem Solution à Distribute data over large clusters Difficulty à How to split work across machines? à Moving data over network is expensive à Must consider data & network locality à How to deal with failures? à How to deal with slow nodes?
- 15. Traditional Approach in Data Management 1TB Hard Drive 3 TB file 1TB of Data 1TB of Data 1TB of Data STORAGE PROCESSING DATA Processor Raw DataProcessed Data
- 16. Addressing Data • Standard Hard Drive data transmission speed 60 – 100 MB/sec • Solid State Hard Drive (SSD) - 250 – 500 MB/sec • Hard Drive capacity growing RAPIDLY (4 – 60 TB) • Online data growth (double every 18 month) • Processing Speed (relatively same growth – Moore law) • Hard Drive transmission speed is relatively FLAT Moving Data IN and OUT of disk is the Bottleneck
- 17. WHY NOW?
- 18. Addressing Data – Digital Universe 0 5 10 15 20 25 30 35 1995 2000 2003 2005 2008 2009 2010 2011 2012 2014 2016 2017 2018 DataGrowthinZettaBytes Digital Universe Growth over time
- 19. Addressing Data – Hard Disk Capacity 0 10000 20000 30000 40000 50000 60000 70000 1991 1998 2003 2005 2007 2008 2009 2010 2011 2012 2014 2016 2017 CapacityinGigaBytes Hard Drive Capacity Growth over time
- 20. Addressing Data – Storage Cost 1200000 100000 10000 800 10 1 0.1 0.003 0.0020 200000 400000 600000 800000 1000000 1200000 1400000 1980 1985 1990 1995 2000 2005 2010 2015 2020 Price$perGBytes Data Storage Cost per Gigabyte AAdamov, CeDAR, ADA University
- 21. Computation Power CPU and GPU 0 500 1000 1500 2000 2500 3000 3500 4000 4500 2001 2002 2005 2006 2008 2009 2010 2012 2013 2014 2015 2017 2018 GFLOPS Computation Power CPU and GPU GPU CPU
- 22. HADOOP ECOSYSTEM
- 23. Hadoop Ecosystem – Big Data Tech Stack STORAGE DATA MANAGEMENT PROCESSING INTELLIGENCE / VISUALIZATION
- 24. Hadoop Core = Storage + Compute storage storage storage storage CPU RAM Yet Another Resource Negotiator (YARN) Hadoop Distributed File System (HDFS)
- 25. BIG DATA COMPUTING SOLUTION
- 26. Timeline of Computing Architecture Traditional Architecture – 2000 Distributed Architecture 2010 + Operating System HARDWARE App App App HARDWARE App App App HYPERVISOR OS OS OS HARDWARE HARDWARE HARDWARE OS OS OS HADOOP HDFS + YARN App App App App App App Virtualized Architecture 2000 +
- 27. Distributed vs Traditional Computing RDBMS Function SAN / NAS DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA Function DATA Function DATA Function DATA Function DATA Function DATA Function DATA Function DATA Function DATA Function DATA Function DATA Function DATA Function DATA Traditional Computing Distributed Computing
- 28. Distributed Architecture of HDFS Rack 1 DN1 DN2 DN3 DN4 Switch Rack 2 DN11 DN12 DN13 DN14 Switch Rack 3 DN21 DN22 DN23 DN24 Switch Rack 4 DN31 DN32 DN33 DN34 Switch CC AA DD BB A Where to write file ADA.txt (blocks A, B, C, D) in HDFS? CLIENT NAMENODE A B C D A – DN32, 11, 14 B – DN01, 22, 23 C – DN12, 02, 04 D – DN34, 12, 14 BCD AAdamov, CeDAR, ADA University
- 29. MapReduce Architecture INPUTDATA OUTPUTDATA Map() Map() Map() Map() Reduce() Reduce() Split [k1, v1] Sort by k Merge [k1, [v1, v2, …, vN]]
- 30. MapReduce Job – Logical View MAP SHUFFLE REDUCE
- 33. How different CPU and GPU? Several Cores Hundreds of Thousands of Cores Application CPU GPU Parallel and Compute- intensive FunctionsGeneral Logic and Serial Functions
- 34. Low Latency vs. High Throughput CPU ALU ALU L2 (cache) ALU ALU CONTROL DRAM GPU DRAM L2 (cache) Hundreds of ALUs Hundreds of ALUs • Optimized for low-latency access to cached datasets • Control logic for out-of- control and speculative executions • Optimized for Data-parallel throughput computation • Architecture tolerant of memory latency • More transistors dedicated to computation
- 35. Interaction between CPU and GPU CPU ALU ALU L2 (cache) ALU ALU CONTROL DRAM GPU DRAM L2 (cache) Hundreds of ALUs Hundreds of ALUs 1. Copy Input Data from CPU Memory to GPU Memory; 2. Load dedicated functions to GPU; 3. Copy computing results from GPU Memory to CPU Memory. 1 2 3
- 36. What is CUDA? • Parallel Programming Model • Includes Memory Model • Can utilize 100’s of CUDA Cores and 1000’s of Parallel Threads • Developer can focus on Parallel Programming abstracting from many low-level operations • Supports heterogeneous systems using CPU+GPU • Implemented in C++ • There are extensions for C, C++, C#, Fortran, Java, Python, Ruby, etc.
- 37. CUDA Kernel • Kernel is a set of computing instructions (small program) designed for GPU; • GPU runs Kernels in 1000’s of parallel threads; • CUDA Threads: • Lightweight • Fast Switching • Massively Parallel
- 38. Logical Architecture vs. Physical Architecture Threads CUDA Core …. Thread Block Streaming Multiprocessor (SM) …. …. Grid …. …. …. …. GPU Unit Executed by Executed by Executed by
- 39. BIG DOES NOT MEAN SLOW AAdamov, CeDAR, ADA University
- 40. SMALL DOES NOT MEAN WEAK AAdamov, CeDAR, ADA University
- 41. Information is the oil of the 21st century, and Analytics is the Combustion Engine
- 42. Q & A ? Dr. Abzetdin Adamov, Email me at: aadamov@ada.edu.az Follow me at: @ Link to me at: www.linkedin.com/in/adamov Visit my blog at: aadamov.wordpress.com