IBM Systems

Anand HaridassChief Engineer POWER Integrated Solutions (BD&A)Senior Technical Staff MemberIndia Systems Development Labanharida@in.ibm.comhttps://www.linkedin.com/in/anandharidass

Big Data & Analytics -POWER Up Your Insights

IBM Systems

Acknowledgement

Sources of these slides are numerous IBM presentations/tutorial/slides

Thank you

Special thanks to Steve Roberts (https://www.linkedin.com/in/steve-roberts-a5246a3b)

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IBM Systems

Agenda

� The Big Picture about Big Data

� Hadoop & Spark Market

� IBM POWER / OpenPOWER

� POWER Big Data Offerings

IBM Systems

Big Data & Analytics

IBM Systems

DATA is THE resource of the 21st CenturyAn unprecedented increase in use of digital devices is causing humungous amount of data to begenerated and captured by businesses. This tremendous amount of digital data, also known as

Big Data has the potential to transform businesses and create value.

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There will be over 200 billion connected devices

There will be over 12 billion machine-to-machine devices

Machine generated data will be 42% of all data

4x more digital data than all the grains of sand on earth

By 20202020

Internet of Things

And this just the beginning ….

IBM Systems

Big data has swept into every sector and business function

7

Retail Telco Financial Services

Energy & Utilities

Pharma Healthcare

Auto Public Sector Industrial

Big data and analytics are impacting innovation across

multiple industries

Dynamic Pricing

Forecasting

Predicating Trends

CRM

Responsive Design

Revenue Models

Fraud Prevention

Risk Mitigation

Power Management

Utility Billing

Personalized Medicine

Target Therapy

PredicativeAnalysis

E-Health

Traffic Reduction

Connected Vehicles

Resource

Planning

Management

Reduce Waste

Consumption Planning

Big Data

Communications Sector

Distribution Sector

PublicSector

Financial Sector

Industrial Sector

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Big Data : Value from Insights

DescriptiveWhat is happening

CognitiveWhat did I learn

Value

PrescriptiveWhat should I do

PredictiveWhat could happen

DiagnosticWhy did it happen

Cognitive computing defines systems that

learn at scale, reason with purpose &

interact with humans naturally. Cognitive

systems are probabilistic, this is a core point

of difference as it means they are not

programmed, instead they have been

trained. Cognitive systems can generate not

just answers to questions but hypotheses,

reassured responses & recommendations

about more complex & meaningful data.

IBM Systems

Infrastructure : Needs to have

9

Evolving BD&A priorities

�Enable emerging BD&A workloads

�Manage elevated service levels

�Increase agility and elasticity

�Reduce costs

Extreme Speed and Flexibility for dynamic, real-time business demands

• Use real workloads/workflows to drive design points that accelerate insights in real-time at the point of impact

• Adapt to business and application changes quickly

Continuous Availability for the most demanding workloads

• To consistently deliver insights to the people and processes that need them

• Efficient high availability & disaster recovery

• Seamless transition to new technology

Ability to manage and analyze data volume, velocity and variety to gain unprecedented business insight

• Innovation across the system elements to minimize data movement and maximize use of all types of data to accelerate decisions

IBM Systems

Hadoop & Spark Market

IBM Systems

What is Hadoop?� Open source project to enable processing of large data sets

� Batch oriented

� Structured, unstructured, semi-structured data

� Written in Java

� Scalable to thousands of machines

� Fault tolerant

� Core components: HDFS, MapReduce, Hadoop Common

Data 1TB

Disk Read 200MB/s

1 server

1 Disk 5000 sec

10 Disks 500 sec

100 server (x10 Disks) 5 sec

IBM Systems

What is Spark?

� Open Source, Apache 2.0, version 1.x

� Written in Scala

� In-Memory, On-Disk, Batch, Interactive, Streaming (Near Real-Time)

� Rapid in-memory processing of resilient distributed datasets (RDDs)

� Multiple Workflows

� Multiple Libraries

� Multiple API’s

Fast flexible engine for big data processing - 10x (on disk) to 100x (in memory)

faster than MapReduce

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+

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Hadoop and Spark Offer Significant Business Benefits

14

Operations Data Warehousing Line of Business and

Analytics

New Business

Imperatives

Big Data Maturity High

High

Low

Data-Informed

Decision Making

• Full dataset analysis

(no more sampling)

• Extract value from

non-relational data

• 360o

view of all

enterprise data

• Exploratory analysis

and discovery

Warehouse

Modernization

• Data lake

• Data offload

• ETL offload

• Queryable archive

and staging

Lower the Cost

of Storage

Business

Transformation

• Create new business

models

• Risk-aware decision

making

• Fight fraud and

counter threats

• Optimize operations

• Attract, grow, retain

customers

Value

IBM Systems

Market trends� Forrester estimates that 100% of all large enterprises will adopt Hadoop and related

technologies such as Spark for big data analytics within the next two years. (Jan/2016)

� Global Hadoop market is expected to garner revenue of $84.6B by 2021, a CAGR of 63.4% during 2016 to 2021. North America accounted for 52% share of overall market revenue in 2015, owing to higher rate of adoption in industries such as IT, banking, & government sector. Europe is anticipated to witness the fastest CAGR of 65.7% during the forecast period (2016 - 2021). (Allied Market Research, Jan/2016)

� Global spending by large enterprises on machine learning solutions is forecast to grow from $416 million to $2,194 million by 2019 at a CAGR of 39.5% (Markets&Markets, May/2015)

Spark will reinvigorate Hadoop

and, in 2016, nine out of every 10

projects on Hadoop will be Spark-

related projects.

By 2020, 40% of all business

analytics software will incorporate

prescriptive analytics built on

cognitive computing functionality

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IBM POWER / OpenPOWER

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Innovation Beyond The Microprocessor

17

Microprocessors alone no longer drive sufficient Cost/Performance improvements

Full System & Stack

Open Innovation Required

Microprocessor Trends

Over The Last 40+ Years

Price/P

erf

orm

ance

Moore’s Law

Processor Technology

2000 2020

Firmware / OS

Accelerators

Software

Storage

Network

IBM Systems

OpenPOWER

18

Accelerated innovation through

collaboration of partners

Accelerated innovation through

collaboration of partners

Amplified capabilities driving

industry performance leadership

Amplified capabilities driving

industry performance leadership

Vibrant ecosystem through open

development

Vibrant ecosystem through open

development

Cloud ComputingHyper-scale & Large scale

Datacenters

High PerformanceComputing & Analytics

Domestic IT Agendas

Industry adoption, Open choice

OpenPOWER Strategy

Moore’s law no longer satisfies performance gain

Numerous IT consumption models

Growing workload demands

Mature Open software ecosystem

Market Shifts

OpenPOWER is an open development community, using the POWER Architecture to serve the evolving needs of customers.

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Fueling ecosystem innovation with OpenPOWER

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Fueling ecosystem innovation with OpenPOWER

230+ Members 24 Countries 6 Continents

100+ Collaborative innovations under way

50+ Hardware and technology providers

…IN 2+ YEARS

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POWER Processor Roadmap

POWER8 Architecture POWER9 Architecture

2014POWER8

12 cores

22nm

New Micro-Architecture

New ProcessTechnology

2016POWER8w/ NVLink

12 cores22nm

EnhancedMicro-

ArchitectureWith NVLink

2017P9 SO24 cores

14nm

New Micro-Architecture

Direct attachmemory

New ProcessTechnology

Optimized for Data-Centric Workloads

Integrated PCIe

CAPI Acceleration / I/O

Scale-Out Datacenter TCO Optimization

Scale-up performance Optimization

Acceleration Enhancements to CAPI and NVLINK

Modularity for OpenPOWER

TBDP9 SU

TBD cores

14nm

EnhancedMicro-

Architecture

BufferedMemory

POWER6 Architecture POWER7 Architecture

2007POWER6

2 cores

65nm

New Micro-Architecture

New ProcessTechnology

2008POWER6+

2 cores

65nm+

EnhancedMicro-

Architecture

EnhancedProcess

Technology

2010POWER7

8 cores

45nm

New Micro-Architecture

New ProcessTechnology

2012POWER7+

8 cores

32nm

EnhancedMicro-

Architecture

New ProcessTechnology

High Frequency

Enhanced RAS

Dynamic Energy Management

Large eDRAM L3 Cache

Optimized VSX

Enhanced Memory Subsystem

Focus on EnterpriseTechnology and Performance Driven

Focus on Scale-Out and EnterpriseCost and Acceleration Driven

2018 - 20P8/9 SO

10nm - 7nm

Existing Micro-

Architecture

FoundryTechnology

Partner ChipPOWER8/9

OpenPOWEREcosystem

DesignTargeting

Partner Markets & SystemsLeveraging Modulatrity

2020+

New Micro-Architecture

NewTechnology

POWER10

New Features and

Functions

Future

TBD

Price, performance, feature, and ecosystem innovation

IBM Systems

POWER8 Processor

Bus Interfaces� Integrated PCIe Gen3� SMP Interconnect� CAPI

Accelerators� Crypto & memory expansion� Transactional Memory � Data Move / VM Mobility

Caches� 64K Data cache (L1)� 512 KB SRAM L2 / core� 96 MB eDRAM shared L3� Up to 128 MB eDRAM L4 (off-chip)

Cores � 12 cores (SMT8)� 8 dispatch, 10 issue, 16 exec pipe� 2X internal data flows/queues� Enhanced prefetching

Memory� Dual memory Controllers � 230 GB/sec Sustained bandwidth

Technology22nm SOI, eDRAM, 15 ML 650mm2

IBM Journal of Research and Development Issue 1 • Date Jan.-Feb. 2015 On IEEE Explore - Link

Energy Management� On-chip Power Management Micro-controller� Integrated Per-core VRM� Critical Path Monitors

IBM Systems

POWER8 is designed & optimized for Big Data & Analytics

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Processorsflexible, fast execution of

analytics algorithms

Memorylarge, fast workspace to

maximize business insight

Cacheensure continuous data load

for fast responses

8 Threads/Core96 Threads/Socket

230GB/sec Sustained Memory

BW

96MB (L3) + 128MB (L4)

Optimized for a broad range of big data & analytics workloads:

Industry Solutions

5XFaster

Supports growth of users, reports and complex

queries

Delivers fast analytics results for real-time

decision-making

Handles large volumes of data for better response

times

IBM Systems

Coherent Accelerator Processor Interface (CAPI)

24

Typical I/O Model Flow

Flow with a Coherent Model

Shared Mem.

Notify Accelerator AccelerationShared Memory

Completion

Virtual addressing & data Caching

Easier programming model

Enables applications not possible on I/O

CAPP PCIe

POWER8 Processor

FPGA

Fu

nctio

n n

Fu

nctio

n 0

Fu

nctio

n 1

Fu

nctio

n 2

CAPI

IBM Supplied POWER Service Layer

DD CallCopy or PinSource Data

MMIO NotifyAccelerator

AccelerationPoll / Int

CompletionCopy or Unpin

Result DataRet. From DDCompletion

40x difference

(13us vs 0.36us)

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POWER8’

25

NVIDIA GPU NVIDIA GPU with NVLink

Power Chip Power Chip

with NVLink

80 GB/sPeak*

PCIe x16

Current GPU Attach Future NVLink GPU Attachment

Graphics Memory

System Memory

Graphics Memory Graphics Memory

System Memory

40+40 GB/s

16+16 GB/s

�CPU to GPU NVLink Enables

� Easier Programming of GPU Accelerators

� Better Application Throughput

� Expanded Set of Accelerated Applications

� IBM-NVIDIA NVLink Acceleration Lab

� Seeking Clients Now

� Apply at accellab@us.ibm.com

IBM Systems

Power Systems: Expanding to serve a broader Linux market

26

Offering OS capability Positioning in the Linux portfolio

Sca

le-u

p E LineE880

E870

E850

Equally run AIX, IBM i and

Linux with IFLs

Enterprise systems; Scale Up

Leadership Performance and Reliability

Utilization Guarantee (PowerVM – 70%/80%)

Flexible, dynamic Capacity on Demand & Enterprise

Pools

Sca

le-O

ut

S LineS824

S822

S814

Equally Run AIX, IBM i

and Linux

Scale out Systems; Commercial Entry

Utilization Guarantee (PowerVM – 65)

High performance, availability and resiliency

L lineS824L

S822L

S812L

Linux Only

Scale out Linux Systems

Price/Performance Leadership vs. X86

PowerVM, KVM

LC lineS812LC

S822LC

Linux Only

Cluster-optimized Linux Systems

Lowest cost Power System

KVM

Tra

dit

ion

al

PO

WE

RE

na

ble

d

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POWER Big Data & Analytics

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Power Systems integrated in-memory database solution

28

� Accelerates existing data analytics without expensive upgrades or replacement

� Easier data administration with no tuning or aggregates

� Factory-installed software reduces time to deployment

Power Systems (POWER8)

• DB2 Advanced Workgroup or Advanced Enterprise Edition

• Red Hat or Ubuntu Linux, AIX

• Optional InfoSphere DataStage for data Extract, Transform and Load

Gain new business insights from reports that complete faster

Reduce time generating reports and focus on business initiatives

BLU Acceleration – Power Systems Edition

20-hour analytics queries in an x86

environment complete in <15 minutesvs. comparable Power server configuration

1 Assumes 82x calculation based on geometric mean calculation giving equal weighting to the report per hour (RPH) improvements in the three categories of simple, intermediate, and complex reports. GEOMEAN(RPH_simple,RPH_intermediate,RPH_complex) = GEOMEAN(18.85,40.07,747.63)=82.66. This is

based on IBM internal tests as of April 7, 2014 comparing IBM DB2 with BLU Acceleration on Power with a comparably tuned competitor row store database server on x86 executing a materially identical 2.6TB BI workload in a controlled laboratory environment. Test measured 60 concurrent user report

throughput executing identical Cognos report workloads. Competitor configuration: HP DL380p, 24 cores, 256GB RAM, Competitor row-store database, SuSE Linux 11SP3 (Database) and HP DL380p, 16 cores, 384GB RAM, Cognos 10.2.1.1, SuSE Linux 11SP3 (Cognos). IBM configuration: IBM S824, 24

cores, 256GB RAM, DB2 10.5, AIX 7.1 TL2 (Database) and IBM S824, 16 of 20 cores activated, 384GB RAM, Cognos 10.2.1.1, SuSE Linux 11SP3 (Cognos). Results may not be typical and will vary based on actual workload, configuration, applications, queries and other variables in a production environment.

IBM Systems

Power Systems Integrated Solution for Analytics

29

� Fast business analytics for increasing data volumes, and concurrent users

� BI and/or predictive reports

� Factory-installed software reduces time to deployment

Power Systems (POWER8)

• Cognos Business Intelligence

• SPSS Modeler Gold, Collaboration & Deployment Services

• AIX, Linux on Power LE

• Optional DB2 with BLU Acceleration

View business performance through stunning dashboards and reports

IBM Analytics Solution – Power Systems Edition

Reduce analytics reporting times

nearly1/2 1and predictive scoring by

up to 1/3 2vs. comparable x86

environments

Generate hypotheses and scoring with recommended actions

CognosSPSS

IBM Systems

� Reduce server footprint with DRAM-FLASH consolidation

� 100% Redis compliant (no application changes)

Game-changing innovation for fast access NoSQL data stores

30

IBM Data Engine for NoSQL

IBM Data Engine for NoSQL

• IBM Power S822L

• CAPI-Attached FPGA Accelerator

• IBM FlashSystem

• Ubuntu Linux

• Redis Software

source: for 24:1 system consolidation ratio (12:1 rack density improvement) based on a single IBM S824, (24 cores, POWER8 3.5 GHz), 256GB RAM, AIX 7.1 with 40 TB memory based Flash replacing 24 HP DL380p, 24 cores, E5-2697 v2 2.7 GHz), 256GB RAM, SuSE Linux 11SP3 . Inbound network limits performance to 1M IOPs in both scenarios, equal capacity (#user, data) in both cases. x86 cost includes 10k$ for 2x 1U switches

Load Balancer

500GB Cache Node

10Gb Uplink

POWER8 Server

Flash Array w/ up to 40TB

After: NoSQL POWER8 + CAPI Flash

WWW10Gb Uplink

WWW

Backup Nodes

500GB Cache Node500GB Cache

Node500GB Cache Node500GB Cache

Node

Before: NoSQL in memory

24U4U

Less is More24:1 physical server consolidation =

6x less rack space

Infrastructure Requirements

- Large Distributed (Scale out)

- Large Memory per node

- Networking Bandwidth Needs

- Load Balancing

Acceptable latency

CAPI

Memory

Conventional PCIe I/O

network

network

network

Ecosystem expansion to include Neo4J, Cassandra, Ubuntu version 15.10 with CAPI API’s open for customers & partners

Update

IBM Systems

IBM Big Data on Power Offerings

31

Stage 1: Prove Value

Stage 2: Scale for Multiple Projects Stage 3: Scale for Mixed Analytics

Digital Start for Big Data on Power

IBM Data Engine for Hadoop and

Spark

IBM Data Engine for Analytics

�Ready access for Power customers

�On Premise or Cloud

� Organization: Line of Business (LOB) or Data Science team

�Simplify operations: easy to deploy & manage

�Advanced resource & storage management

�Better resilience for big data

�Spark: 2X better price perf vs x86

� Organization: LOB or Data Science team

�Designed for consolidation and mixed analytics workloads: streams, at rest, text

�Lowest $/TB and less than half storage infrastructure

�Leadership resilience for big data environment

�Adapt and scale to your changing analytics needs

� Organization: IT infrastructure team supporting LoB’sand data team

Limited data investment per project, often <10TB

Single project, limited use cases

Moderate data investment per project50TB to PB

Many independent use case projects across LOB’s

Significant data investment per project1/2 to multi PB

Multiple use cases with diverse SLA’s

Now on IBM Cloud

Marketplace

31

IBM Systems

IBM Data Engine for Hadoop and Spark: IDE-HS

OpenPOWER

IOP +

OpenPOWER

IOP +

OpenPOWER

IOP +

Spectrum Scale FPO Option

• Internal replicated disk

• POSIX compliant

• Encryption/replication

Opt. SpectrumSymphony

• Higher utilization

• Shared cluster

• Better throughput

OpenPOWER (POWER8)

• 2X x86 core performance

• Lowest cost Power HW

Solution

• Pre-assembled/tested cluster

• On-site services

• Lower risk & faster time to value

IBM Open Platform

• Open Hadoop

• Spectrum Scale /

Symphony Options

PlatformCluster Mgr.

Simplified physical

cluster management

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IBM Data Engine for Hadoop and Spark

Single vendor support

Up to 2x better price performance for Spark workloads*

Delivered as a fully integrated cluster ready to run

OpenPOWER innovation with IBM S812LC servers

�Optimized configurations for Hadoop or Spark workloads

�Based on S812LC servers with up to 14*6TB disk drives per server

�Optionally preloaded with IBM Open Platform

�Simplify operations – easy to deploy and manage

�Adapt and scale to your changing analytics needs

OpenPOWER innovation with IBM Open Platform with Apache Hadoop for a high performance, storage dense and fully integrated cluster offering.

• All results are based on IBM Internal Testing of 3 SparkBench benchmarks consisting of SQL RDD

Relation, Logistic Regression, SVM

IBM Systems

IBM Data Engine for Analytics: IDEA

Platform

Cluster Mgr.

POWER8

BigInsights

POWER8

BigInsights

POWER8

BigInsights

Spectrum

Symphony

Spectrum Scale ESS

• One copy of data

• POSIX compliant

• Erasure coding

• Encryption/replication

POWER8

• 2X x86 core performance

• Fewer nodes

BigInsights

• Industry standard Hadoop

• Enterprise capabilities

Solution

• Grow disk/CPU separately

• Pre-assembled/tested cluster

• On-site services

• Lower risk & faster time to value

Simplified physical

cluster management

• Higher utilization

• Shared cluster

• Better throughput

Spec ScaleSpec ScaleSpec Scale

IBM Systems

IBM Data Engine for Analytics

Single vendor support

Less than half storage infrastructure*

One third rack space reduction vs x86 at Petabyte scale

Lowest $/TB and over a third more usable storage**

� Simplify operations – easy to deploy and manage

� Designed for mixed analytics workloads: streams, at rest, text

� Optionally preloaded with IBM Open Platform

� Enterprise grade Hadoop with advanced resource and storage management

� Adapt and scale to your changing analytics needs

A fully integrated solution with software and infrastructure optimized for Big Data & Analytics

* Compared with a standard triple replica Hadoop configuration** List price vs full rack configuration vs Oracle Big Data Appliance with data connectors and BigSQL

Appliance-Like but much more Versatile!

S822L

35

IBM Systems36

Storage Intensive

Com

pute

Inte

nsiv

e

Add M

ore

Serv

ers

Add More Storage

• Add servers or storage or both as needed

• Adjust compute to storage ratio as workload needs change

• Standard Hadoop configs with local storage and triple replica can result in overprovisioned compute to meet the storage demands

• Data Engine for Analytics allows right sizing of compute and storage independently to create an optimized configuration

Data Engine for Analytics offers Independent Scaling of Servers & Storage

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� Streaming and SQL benefit from High Thread Density and Concurrency

• Processing multiple packets of a stream and different stages of a message stream pipeline

• Processing multiple rows from a query

� Machine Learning benefits from Large Caches and Memory Bandwidth

• Iterative Algorithms on the same data

• Fewer core pipeline stalls and overall higher throughput

� Graph also benefits from Large Caches, Memory Bandwidth and Higher Thread Strength

• Flexibility to go from 8 SMT threads per core to 4 or 2

• Manage Balance between thread performance and throughput

� Headroom

• Balanced resource utilization, more efficient scale-out

• Multi-tenant deployments

POWER Advantages for Spark

IBM Systems 38 38

System Performance of Spark on POWER

Machine Learning SQL Graph

1.7X

IBM Systems

More efficient use of resources (Spark 1TB Logistic Regression Example)

39

0

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CPU POWER

User% Sys% Wait%

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10

20

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100

10:1

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5

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10:2

8

CPU Haswell

User% Sys% Wait%

-1200

-1000

-800

-600

-400

-200

0

200

400

600

800

22:4

4

22:4

4

22:4

4

22:4

5

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22:5

0

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22:5

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22:5

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22:5

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22:5

4

MB

/se

c

Network I/O POWER

Total-Read Total-Write (-ve)

-600

-500

-400

-300

-200

-100

0

100

200

300

400

10:1

41

0:1

41

0:1

51

0:1

51

0:1

61

0:1

61

0:1

71

0:1

71

0:1

81

0:1

81

0:1

91

0:1

91

0:2

01

0:2

01

0:2

11

0:2

11

0:2

21

0:2

21

0:2

31

0:2

31

0:2

41

0:2

41

0:2

51

0:2

51

0:2

61

0:2

61

0:2

71

0:2

71

0:2

81

0:2

81

0:2

9

MB

/se

c

Network I/O Haswell

Total-Read Total-Write (-ve)

POWER

� CPU headroom to host higher density

� More data pushed over network due to higher thread density

Haswell

� CPU fully pegged on just this workload

� Underutilizing the Network Resource

IBM Systems

What’s in the works ?

40

GPUs and FPGAs for Compute offload, Machine Learning, Graph and other specialized acceleration

CAPI Flash for Memory consolidation/expansion, and Storage acceleration

RDMA for better latency, better network utilization, lower CPU utilization, lower Memory utilization

Please Look For Big Announce @ EDGE 2016

IBM Systems

Apache Spark on IBM POWERTry Open Source Spark on Softlayer Power Servers

• Ideal for Initial Trials of Spark on Power leading to On Premise deals or client continuing with Softlayer• Experience improved response times for Data Scientist graphing workloads using Zeppelin notebook*

Marketplace link: https://www.ibm.com/marketplace/cloud/big-data-and-analytics/us/en-usQuickstart Guide available: http://www.ibm.com/developerworks/library/l-start-guide-to-apache-bigtop-trs/