1. © 2012 IBM Corporation1
Software and Hardware Infrastructures to conquer Data
Explosion in Life Science - Life Science Network Basel
Romeo Kienzler
Data Scientist and Architect, Pos. Graduate in Information Systems and Bioinformatics
IBM Innovation Center Zurich romeo.kienzler@ch.ibm.com
https://www.ibm.com/developerworks/mydeveloperworks/profiles/user/RomeoKienzler

2. © 2012 IBM Corporation2
Outline
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Data Growth
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Data Growth in Life Science
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BigData in Life Science
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How to address BigData?
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Outlook

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3
Data Growth
Data AVAILABLE to an
organization
data an organization can
PROCESS
Missed
opportunity
100 Million Tweets are posted every day, 35 hours of video are being uploaded every
minute,6.1 x 10^12 text messages have been sent in 2011 and 247 x 10^9 E-Mails passed
through the net.80 % spam and viruses. => Filtering is more and more important.
Up to 2003 the same amount of data has been produced as between 2003 and now

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New Data Sources in Life Sciences
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DNA (RNA) Sequencing
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Next-Generation Sequencing
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DNA Transistor
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Imaging and Video
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Unstructured Text

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Data Growth in Life Sciences
Source: www.osehra.org

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Data Growth in Life Sciences
Source: www.crops.org

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Examples - NGS

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Images and Videos
Source: www.phys.org

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Examples – Text Analytics
Source: www.theglobalistreport.com

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Watson

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SIIB (Strategic IP Insight Platform)
Integrated chemical, biological and textual search
Deep analytics on scientific literature and patents
Aggregation of world wide Patent Data and scientific literature (30M+ docs) with ongoing updates

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The challange
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Store a huge amount of data
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Process a huge amount of data
(incl. Search/Find)
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Don't consume too much energy

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Use many Hard Drives

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Use many Hard Drives

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Use many Hard Drives - Limits
(*) Given a Disk Capacity of 25TB
300 Crashes per Day, Data Loss after two weeks

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Separate the Signal From the Noise¹
¹http://www.ibmsystemsmag.com/power/businessstrategy/BI-and-Analytics/signal_noise/

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Store only what you need

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Use many CPU's
Supercomputer before
➔
Weather
➔
Atom Bombs
➔
Science
➔
Crash Tests
Supercomputer in a Rack
➔
18 TB Main Memory, 1008 CPU Cores, 113 TFLOPS (1st
TOP500 2013: 17590 TFLOPS 2004: 71 TFLOPS)

19. © 2012 IBM Corporation19

20. © 2012 IBM Corporation20
Use specialized CPU's: GPUs
Source: www.ethz.ch
Source: www.nvidia.com

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Use specialized CPU's: FPGA's
Source: www.virtex.com

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Example FPGA: IBM Pure Data
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Up to 1,28 PB Storage
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Up to 10 Racks
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Up to 500 GigaByte/s Throughput
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Up to 1120 FPGA + 1120 Intel CPU Cores / 960 Hard Drives

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Example FPGA: Conveycomputers
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Accelerates BWA by 15x
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Accelerates Smith-Waterman
Source: www.conveycomputer.com

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Example: Algorithms
Source: www.biomedcentral.com/1471-2105/9/S2/S10

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Example: Cloud
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Managed Infrastructure
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Dynamic Provisioning
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Specialized HW
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SaaS
Source: www.basespace.illumina.com

26. © 2012 IBM Corporation26
Conclusion
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Main BigData Sources are Sequences and Plain Text
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Many others to come (e.g. Images and Videos)
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Store Data on many Commodity Hard Drives (Energy Problem not solved)
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Filter Signal from Noise
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Process Data on many CPU's
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Usage of specialized Hardware / CPU's
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Research in performance of algorithms

27. © 2012 IBM Corporation27
Outlook
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Currently very heterogeneous infrastructures
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Trends:
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Virtualization
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Standardization
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Consumerization
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Limits
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Space
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Energy consumption
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What shall I do?
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RELAX

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The future will be full of surprises
A battery powered pocket size super
computer?
Raspberry Pi
Parallela

29. © 2012 IBM Corporation29
Acknowledgements
Slides 14 – 16 & 21 have been taken from a
Keynote speech of Axel Köster, IBM Germany