Informix & IWA : Operational analytics performance

Operational Analytics on Informix:
Architecture and Performance evaluation
Jantz Tran Intel – Database Performance
Keshava Murthy IBM Informix Development

Agenda
• Operational analytics
– What is it? Requiremens & challenges.
• Operational analytics with Informix
– Complete lifecycle discussion.
• Intel® Technology & Roadmap
– Scaling on Xeon® E7 Platform
• Performance work and analysis for Informix on
Intel

• Operational analytics
– Focus on excellence in operations
– Operations of most organizations are complex & multi-
faceted
• Supply chain, production processes, people, partners, etc
• HR, Sales, IT, etc
• More than Efficiency, operational excellence
needs effective, smarter processes
• Customized experience, repeatable at scale
What is Operational analytics?

Challenges in Operational Excellence
• Respond quickly to shifts in reality
• React to competition quickly
• Continuously lower the cost
• IT Challenge:
– handle volume and response times a modern business
requires
– or use people to provide flexibility to respond to
developing situation
• False choice
– System should handle volume, velocity & be flexible

Customer Conversion Acquisition Campaign Response
Customer ChurnRiskFraud
© 2013 Decision Management Solutions

“Most discussions of decision making assume only
senior executives make decisions or that only
senior executives’ decisions mater.
This is a dangerous mistake”
-- Peter Drucker

• What to change?
• What to change to?
• How to cause the change?

Multiplying power of operational analytics
Strategy
Tactics
Operations
Low High
Economic Impact
© 2013 Decision Management Solutions

Business Analytics
• Traditionally, business analytics is on customer
opportunity and risk management
• Quickly detect shifts in reality
• Make reaction part of the routine operations.

The Changing World of BI Analytics
• Advanced Analytics
– Improved analytic tools and techniques for statistical and predictive
analytics
– New tools for exploring and visualizing new varieties of data
– Operational intelligence with embedded BI services and BI
automation
• Data Management
– Analytic relational database systems that offer improved
price/performance and libraries of analytic functions
– In-memory computing for high performance
– Non-relational systems such as Hadoop for handling new types of
data
– Stream processing/CEP systems for analyzing in-motion data

Beyond the EDW: Optimized Platforms
Copyright © BI Research, 2013

Use Case Application Example
Real-Time
Monitoring & Analytics
In-line fraud detection to reduce financial losses
caused bystolen credit cards
Near-Real-Time
Analytics
Next best customer offer to the channel to
increase customer satisfaction & reduce churn
Data Integration
Hub
Collect and manage all sales-related detailed data
(POS, web, supply chain) for down stream analysis
Analytics
Accelerator
Offload & boost the performance of selected
financial analyses to increase satisfaction/retention
of key clients
New LOB
Analytic Application
Manage & monitor spot buying on
web advertising exchanges
Investigative Computing
Platform
Evaluate the effectiveness of
different social computing channels
Copyright © BI Research, 2013

Warehousing Slide – End to End

Overview of
Informix Warehouse Accelerator

• Data Warehouse query Performance without Perspiration
• Consistent query performance without tuning efforts.
• More questions, faster answers, better data driven decisions & business insights
• SKECHERS: Acceleration from 60x to 1400x – average acceleration of 450x
Motivation

Informix Database Server
Informix warehouse Accelerator
BI Applications
Step 1. Install, configure,
start Informix
start Accelerator
Step 3. Connect Studio to
Informix & add accelerator
Step 4. Design, validate,
Deploy Data mart
Step 5. Load data to
accelerator
Ready for Queries
IBM Smart Analytics
Studio
Step 1
Step 2
Step 3
Step 4
Step 5
Ready
Informix Ultimate Warehouse edition

17
Informix Primary
Informix warehouse Accelerator
BI Applications
start Informix
start Accelerator
Step 3. Connect Studio to
Informix & add accelerator
Step 4. Design, validate,
Deploy Data mart from
Primary, SDS, HDR, RSS
Step 5. Add IWA to sqlhosts
Load data to
Accelerator from any node.
Ready for Queries
IBM Smart Analytics
Studio
Step 1
Step 3
Step 4
Step 5
Ready
Informix Warehouse Accelerator – 11.70.FC5. MACH11 SupportInformix Warehouse Accelerator – 11.70.FC5. MACH11 Support
Informix
SDS1
Informix
SDS2
Informix
HDR
Secondary
Informix
RSS
Step 2

Design DM by
workload analysis or
manually
Deployed datamart
Datamart
Deleted
Datamart in USE
Datamart Disabled
Partition based refresh
Trickle feed refresh
Deploy
Load
Drop
Disable
Enable Drop
Typically,
300 GB/hr
10 GB under 3 mins
Online operation
Stages & Options for data loading to IWA

Scaling in Westmere: Data Warehouse Setup.
• TPC-DS Schema;
web_sales
• Mart Data size: 1
terabytes
• web_sales, 4.1 billion
rows
– Fact with 34 partitions
• Dimensions: 13, non
partitioned.
4.1 billion
73,049 66
22
86,400
20
7,20015 million
66
30 million
1.9 million
1,800
360,000
3600

Scaling in Westmere: Results
Analytics on Warehouse schema

Store_sales data mart
Analytics on Warehouse schema

23
IWA 1st
Release
On SMP
SMB: IGWE
Scale out: IWA
on Blade ServerWorkload Analysis Tool
More Locales
Data Currency
IWA: Roadmap
Partition Refresh
MACH11 support
Solaris on Intel
Automatic data refresh
Union queries
Derived tables
OAT Integration
SQL/OLAP for IWA
Timeseries Acceleration
11.7xC2
11.7xC5
12.1xC1
11.7xC3
11.7xC4
2012 IIUG
2013 IIUG
TS Data Refresh
improvements;
Quicker to analysis
12.10.xC2

Intel Inside® : Intel®
Technology & Roadmap

25
INTEL/IWA: Breakthrough technologies for
performance
1
2
3
4
5
6
7 1
2
3
4
5
6
7
1. Large memory support
64-bit computing; System X with MAX5 supports up
to 6TB on a single SMP box; Up to 640GB on each
node of blade center. IWA: Compress large dataset
and keep it in memory; totally avoid IO.
7. Multi-core, multi-node environment
Nehalem has 8 cores and Westmere 10 cores. This trend is
expected to continue. IWA: Parallelize the scan, join, group
operations. Keep copies of dimensions to avoid cross-node
synchronization.
4. Virtualization Performance
Lower overhead: Core micro-architecture
enhancements, EPT, VPID, and End-to-End
HW assist IWA: Helps informix and IWA to
seemlessly run and perform in virtualized
environment.
5. Hyperthreading
2x logical processors; increases processor
throughput and overall performance of threaded
software. IWA: Does not exploit this since the
software is written to avoid pipeline flushing.
3. Frequency Partitioning
IWA: Enabler for the effective parallel access
of the compressed data for scanning.
Horizontal and Vertical Partition Elimination.
2. Large on-chip Cache
L1 cache 64KB per core, L2 cache is 256KB per
core and L3 cache is about 4-12 MB.
Additional Translation lookaside buffer (TLB).
IWA: New algorithms to avoid pipeline
flushing and cache hash tables in L2/L3 cache
6. Single Instruction Multiple Data
Specialized instructions for manipulating
128-bit data simultaneously. IWA:
Compresses the data into deep columnar
fashion optimized to exploit SIMD. Used in
parallel predicate evaluation in scans.

Tick-Tock Development Model:Tick-Tock Development Model:
Sustained Microprocessor LeadershipSustained Microprocessor Leadership
Intel®
Core™
Microarchitecture
Intel®
Core™
Microarchitecture
TOCK
New
Micro-
architecture
MeromMerom
65nm65nm
TICK
PenrynPenryn
New
Process
Technology
45nm45nm
Intel® Microarchitecture
Codename Nehalem
Codename Nehalem
TOCK
New
Micro-
architecture
NehalemNehalem
45nm45nm
TICK
WestmereWestmere
32nm32nm
New
Process
Technology
Codename Sandy
Bridge
Codename Sandy
Bridge
TOCK
SandySandy
BridgeBridge32nm32nm
New
Micro-
architecture
TICK
IvyIvy
BridgeBridge22nm22nm
New
Process
Technology
Codename Haswell
Codename Haswell
TOCK
HaswellHaswell
22nm22nm
New
Micro-
architecture
TICK
FutureFuture
14nm14nm
New
Process
Technology

Mainstream
Enterprise
Best combination of
performance, power efficiency,
and cost
High Performance Computing &
Workstations
Bandwidth-optimized for high
performance analytics & visualization
Small
Business
Economical and more
dependable vs. desktop
Increasing capability
Cloud Computing
Efficient, secure, and open platforms for
Internet datacenters and IAAS
Entry Servers and
Workstations
More features and performance than
traditional desktop systems
Enterprise Server
Versatility for infrastructure apps (up to 4S)
Scalable
Enterprise
Top-of-the-line performance,
scalability, and reliability
Cloud Computing
Highest virtualization density and advanced
reliability for private cloud
Mission Critical
Performance and reliability for the most
business critical workloads with outstanding
economics
High Performance Computing
Greater scaling and memory capacity
27
Intel®
Xeon®
Processor Family for Business

Intel®
Xeon®
Processor
E7-8800/4800/2800 Product Families
Building on Xeon®
7500 Leadership Capabilities
• More performance within same
max CPU TDP as Xeon 7500
• Lower partial active & idle power
via Intel Intelligent Power
Technology2
• Support for Low Voltage-DIMMs3
• Reduced power memory buffers4
More Efficient
• Supports 32GB DDR3 DIMMs (2TB per 4-
socket system)1
More Expandable
More Security & RAS
• 10 cores / 20 threads
• 30MB of last level cache
More Performance
E7-4800 E7-4800
E7-4800 E7-4800
SECURITY
• Intel®
Advanced Encryption
Standard-New Instructions
• Intel®
Trusted Execution
Technology (TXT)
RELIABILITY, AVAILABILITY, SERVICEABILITY
• Enhanced DRAM Double Device Data Correction
• Fine Grained Memory Mirroring
1. Up to 64 slots per standard 4 socket system x 32GB/DIMM = 2TB
2. Uses similar core and package C6 power states enabled on Intel Xeon 5500/5600 series processors. Requires OS support.
3. Savings dependent on workload and configuration.
4. Memory buffer power savings of up to 1.3W active and 3W idle per buffer per Intel estimates. Slightly more savings when used with LV DIMMs
Delivers more Performance, Expandability and RASDelivers more Performance, Expandability and RAS
while improving Energy Efficiencywhile improving Energy Efficiency
Delivers more Performance, Expandability and RASDelivers more Performance, Expandability and RAS
while improving Energy Efficiencywhile improving Energy Efficiency

29
Intel® Xeon® 7500/E7 8 Socket Configuration
4+4 (8S)
Up to 10 cores and 2.4 Ghz
per CPU
Support 8 socket mode by
combining 2 systems via
external QPI links
Memory Configuration
 4TB in 8 socket server
 6TB in 8 socket + MAX5
 Continued 1066MHz
support
IBM® System
x3850 X5

30
• Machine Check Architecture (MCA)
recovery (MCA-R)
• Machine Check Architecture (MCA)
recovery (MCA-R)
Memory
• Inter-socket Memory Mirroring
• Intel®
Scalable Memory
Interconnect (Intel® SMI) Lane
Failover
• Intel®
SMI Clock Fail Over
• Intel®
SMI Packet Retry
• Memory Address Parity
• Failed DIMM Isolation
• Memory Board Hot Add/Remove
• Dynamic Memory Migration*
• OS Memory On-lining *
• Recovery from Single DRAM
Device Failure (SDDC) plus
random bit error
• Memory Thermal Throttling
• Demand and Patrol scrubbing
• Fail Over from Single DRAM
Device Failure (SDDC)
• Enhanced DRAM Double Device
Data Correction
• Memory DIMM and Rank Sparing
• Intra-socket Memory Mirroring
• Mirrored Memory Board Hot
Add/Remove
• Inter-socket Memory Mirroring
• Intel®
Scalable Memory
Interconnect (Intel® SMI) Lane
Failover
• Intel®
SMI Clock Fail Over
• Intel®
SMI Packet Retry
• Memory Address Parity
• Failed DIMM Isolation
• Memory Board Hot Add/Remove
• Dynamic Memory Migration*
• OS Memory On-lining *
• Recovery from Single DRAM
Device Failure (SDDC) plus
random bit error
• Memory Thermal Throttling
• Demand and Patrol scrubbing
• Fail Over from Single DRAM
Device Failure (SDDC)
• Enhanced DRAM Double Device
Data Correction
• Memory DIMM and Rank Sparing
• Intra-socket Memory Mirroring
• Mirrored Memory Board Hot
Add/Remove
Advanced Reliability Starts With Silicon
Intel® Xeon® processor E7 family RAS Capabilities
I/O Hub
• Physical IOH Hot Add
• OS IOH On-lining*
• PCI-E Hot Plug
• Physical IOH Hot Add
• OS IOH On-lining*
• PCI-E Hot Plug
CPU/Socket
• Machine Check Architecture
(MCA) recovery (MCA-R)
• Corrected Machine Check
Interrupt (CMCI)
• Corrupt Data Containment Mode
• Viral Mode
• OS Assisted Processor Socket
Migration*
• OS CPU on-lining *
• CPU Board Hot Add at QPI
• Electronically Isolated (Static)
Partitioning
• Single Core Disable for Fault
Resilient Boot
• Machine Check Architecture
(MCA) recovery (MCA-R)
• Corrected Machine Check
Interrupt (CMCI)
• Corrupt Data Containment Mode
• Viral Mode
• OS Assisted Processor Socket
Migration*
• OS CPU on-lining *
• CPU Board Hot Add at QPI
• Electronically Isolated (Static)
Partitioning
• Single Core Disable for Fault
Resilient Boot
Intel®
QuickPath Interconnect
• Intel QPI Packet Retry
• Intel QPI Protocol Protection via
CRC (8bit or 16bit rolling)
• QPI Clock Fail Over
• QPI Self-Healing
• Intel QPI Packet Retry
• Intel QPI Protocol Protection via
CRC (8bit or 16bit rolling)
• QPI Clock Fail Over
• QPI Self-Healing
Advanced reliability features work to maintain data integrityAdvanced reliability features work to maintain data integrityAdvanced reliability features work to maintain data integrityAdvanced reliability features work to maintain data integrity

2012 2013/Future
Roadmap
2S Efficient
Performance
Intel® Xeon® processor E5-2600 product family
2 sockets, up to 8C/16T per sockets, up to 20MB shared cache, “Sandy Bridge” microarchitecture
Future Intel®
Micro-
architecture
codename
Ivy Bridge
4S Efficient
Performance
Intel® Xeon® processor E5-4600 product family
4 sockets, up to 8C/16T per sockets, up to 20MB shared cache, “Sandy Bridge” microarchitecture
31
Expandable
Intel®
Xeon®
processor E7-8800/4800/2800
product families
2-8 sockets, up to 10C/20T per socket, up to 30MB shared cache, “Westmere” microarchitecture

Operational Analytics Performance

Customers Brokers Market
READ-WRITE
•Market-Feed
•Trade-Order
•Trade-Result
•Trade-Update
•Security-Detail
•Trade-Lookup
•Trade-Status
READ-ONLY
•Broker-Volume
•Customer-Position
•Market-Watch
Invoke the following transactions …
… against the following data
Customer Data Brokerage Data Market Data
Customers Brokers Market
READ-WRITE
•Market-Feed
•Trade-Order
•Trade-Result
•Trade-Update
•Security-Detail
•Trade-Lookup
•Trade-Status
READ-ONLY
•Broker-Volume
•Market-Watch
READ-WRITE
•Market-Feed
•Trade-Order
•Trade-Result
•Trade-Update
•Security-Detail
•Trade-Lookup
•Trade-Status
READ-ONLY
•Broker-Volume
•Market-Watch
Invoke the following transactions …
… against the following data
Customer Data Brokerage Data Market Data
TPCE Environment

“Real-world” basis for TPC-E
Network
Network
Database
Services
Application
And
Business Logic
Services
Presentation
Services
Workstation
Laptop
Hand-held
Cell phone
Examples of
User Interfaces
Stock Market
Exchange
Example of
External Business
Modeled Business
Legend
Customer
Sponsor Provided
Stock Market
Network
Network
Database
Services
Application
And
Business Logic
Services
Presentation
Services
Workstation
Laptop
Hand-held
Cell phone
Examples of
User Interfaces
Stock Market
Exchange
Example of
External Business
Modeled Business
Network
Network
Database
Services
Application
And
Business Logic
Services
Presentation
Services
Workstation
Laptop
Hand-held
Cell phone
Examples of
User Interfaces
Stock Market
Exchange
Example of
External Business
Modeled Business
Legend
Customer
Sponsor Provided
Stock Market
LegendLegend
Customer
Sponsor Provided
Stock Market
Customer
Sponsor Provided
Stock Market

OLAP queries
SELECT SECURITY.s_name,
       exchange.ex_name         AS ex_namekey,
       Sum(daily_market.dm_vol) AS dm_vol
FROM   exchange,
       daily_market daily_market,
       SECURITY
WHERE  exchange.ex_id = SECURITY.s_ex_id
       AND SECURITY.s_symb = daily_market.dm_s_symb
       AND MONTH(s_52wk_high_date) = MONTH(s_52wk_low_date)
GROUP  BY SECURITY.s_name,
          exchange.ex_name;

OLAP queries
SELECT T0.c0 AS ct_dtskey,
T0.c1 AS ct_amt,
T0.c1 AS c3,
T0.c2 AS c4,
Min(T0.c3)
OVER (
PARTITION BY T0.c0) AS ct_amt2
FROM (SELECT DISTINCT cash_transaction.ct_dts AS C0,
Sum(cash_transaction.ct_amt)
OVER (
PARTITION BY cash_transaction.ct_dts) AS C1,
COUNT(cash_transaction.ct_amt)
OVER (
PARTITION BY cash_transaction.ct_dts) AS C2,
Stddev(cash_transaction.ct_amt)
OVER (
PARTITION BY cash_transaction.ct_dts) AS C3
FROM cash_transaction cash_transaction
WHERE DATE(ct_dts) BETWEEN DATE('2005-01-04') AND DATE('2005-01-05')
AND ct_name LIKE 'Stop-Loss%') T0;

Intel® Xeon® E7-8870:
• Hardware setup
– Intel® Xeon® E7-8870 processor – 4 socket (40C/80T) and
8 socket (80C/160T) configurations
• 2.4 GHz, 30MB last level shared cache
– 10 TB storage
– 2 TB RAM
• Software Setup
– Informix and Informix Warehouse Accelerator: v11.70.FC7
and Informix 12.10
– Both Informix and IWA on the same machine.

Data Setup
• Data Loading
– 300 GB of starting data set
– Data size is about nnn GB including indexes.
• TPCE is heavily indexed for performance
– As we run the OLTP workload, the data size increases.

IDS 12.10 on Intel Westmere: Multi user scaling
0
500
1000
1500
2000
2500
3000
3500
4000
4500
1 2 4 8 16 32 50
Concurrent User Count
QueryTime(seconds)
4s-NoHT 4s+HT 8sNoHT 8s+HT

IDS 12.10 on Intel Westmere: Multi user scaling
0
500
1000
1500
2000
2500
3000
3500
4000
4500
1 2 4 8 16 32 50
Concurrent User Count
NumberofQueriesperhour
4sNoHT 4s+HT 8sNoHT 8s+HT

4 324.95 232.42 186.94 148.19
8 645.59 468.88 368.41 306.8
16 1365.42 935.23 744.3 575.64
32 2583.4 1930.16 1560.75 1167.86
50 4107.19 2985.27 2058.1 1810.92
4sHT28s+HT4s0HT28sHT
1 60.61549 67.1297
2 58.0058 63.17112
4 57.52885 63.75957
8 57.06563 65.43252
16 54.5107 61.55063
32 60.41457 60.50586
50 50.10969 60.66185
56.89296 63.17304 60.033
4s0HT 4sHT 8s0HT 8sHT
1 1659.751 2174.241 2738.163 3238.866
2 1741.444 2412.262 3002.189 3818.616
4 1772.58 2478.272 3081.203 3886.902
8 1784.414 2456.919 3126.951 3754.889
16 1687.393 2463.565 3095.526 4002.502
32 1783.696 2387.367 2952.427 3945.678
50 1753.023 2411.842 3498.372 3975.88
0
500
1000
1500
2000
2500
3000
1 2
QueryTime(seconds
0
500
1000
1500
2000
2500
1 2 4
Concurr
NumberofQueries
IDS 12.10 on Intel Westm
1000
1500
2000
2500
3000
3500
4000
4500
QueryTime(seconds)
8s over 4s (no HT) 8s over 4s (with HT) 4s-No

12.10 Improvement: Average: 988% Geomean: 550%
Informix 11.70 vs 12.10 Operational Analytics performance
Intel Westmere - 8 Socket with HT
0
50
100
150
200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Operational Analytics Queries
QueryTimes(seconds)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Improvement(%ge)
12.10 Improvement Informix 11.70 Informix 12.10

Intel Westmere - 8 Socket (No HT)
0
50
100
150
200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
QueryTimes(seconds)
0
1000
2000
3000
4000
5000
6000
Improvement(%ge)

Intel Westmere - 4 Socket (With HT)
0
50
100
150
200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
QueryTimes(seconds)
0
1000
2000
3000
4000
5000
6000
Improvement(%ge)

Intel Westmere - 4 Socket (No HT)
0
50
100
150
200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
QueryTimes(seconds)
0
1000
2000
3000
4000
5000
6000
Improvement(%ge)

IBM Informix* Database
Scale-up Optimized for Intel Architecture
Baseline
Intel Xeon
processor E7-4870
Informix* v11.7
Up to 45%
Intel®
Xeon®
processor E7-8870
Informix* v11.7
Informix* v11.7
1.45x
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as
SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those
factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated
purchases, including the performance of that product when combined with other products.
*Other brands and names are the property of their respective owners

Informix* v12.1
1.6x
Up to 60%
Intel®
Xeon®
processor E7-8870
Informix* v12.1
Intel Xeon
processor E7-4870
Informix* v12.1

Baseline
Intel Xeon
processor E7-4870
Informix* v11.7
Up to 550%
Intel Xeon
processor E7-8870
Informix* v12.1
Intel®
Xeon®
processor E7-8870
Informix* v11.7
Up to 540%
Intel Xeon
processor E7-4870
Informix* v12.1
Informix* v11.7 Informix* v12.1
Up to 5.4x
Up to 5.5x

Informix OLTP & OLAP Performance
0
10000
20000
30000
40000
50000
60000
0usr 1usr 2usr 4usr 8usr 16usr 32usr
OLTPAverageTransactionspersecond
0
20
40
60
80
100
120
OLAP-AverageAnalyticalqueriesper
minute
OLTP (with OLAP) OLAP only OLAP (with OLTP)
Number of concurrent OLAP usersNumber of concurrent OLAP users

IWA Resources
• IBM Informix Infocenter:
http://ibm.co/fMcUDg
• Martin’s blog: http://ibm.co/Ts0cll
• Fred Ho’s blog:
http://ibm.co/T9FaNy
• Keshav’s blog:
http://ibm.co/RQXExL

Informix Publications
Bulletin of the Technical Committee on Data Engineering: March 2012
Vol. 35 No. 1
Real Time Business Intelligence. September 2, 2011 - Seattle, United States
IBM Data management Magazine: Supercharging
the
data wharehouse while keeping the costs down.
2012 Bloor Report: IBM Informix in hybrid workload
environments
2012 Ovum Analyst report: Informix Accelerates Analytic Integration
into OLTP
DBTA Article: Empowering Business Analysts with Faster Insights
http://youtu.be/xJd8M-fbMI0

Jantz Tran Intel jantz.c.tran@intel.com
Keshava Murthy IBM rkeshav@us.ibm.com

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• Intel processor numbers are not a measure of performance. Processor numbers differentiate features within each
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depending on hardware, software and system configuration. For more information, visit
http://www.intel.com/technology/turboboost
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subsidiaries in the United States and other countries.
• Copyright © 2012, Intel Corporation. All rights reserved.

Intel - Legal Disclaimers Performance
• Performance tests and ratings are measured using specific computer systems and/or components and reflect the
approximate performance of Intel products as measured by those tests. Any difference in system hardware or
software design or configuration may affect actual performance. Buyers should consult other sources of information
to evaluate the performance of systems or components they are considering purchasing. For more information on
performance tests and on the performance of Intel products, Go to:
http://www.intel.com/performance/resources/benchmark_limitations.htm.
• Intel does not control or audit the design or implementation of third party benchmarks or Web sites referenced in this
document. Intel encourages all of its customers to visit the referenced Web sites or others where similar
performance benchmarks are reported and confirm whether the referenced benchmarks are accurate and reflect
performance of systems available for purchase.
• Relative performance is calculated by assigning a baseline value of 1.0 to one benchmark result, and then dividing
the actual benchmark result for the baseline platform into each of the specific benchmark results of each of the other
platforms, and assigning them a relative performance number that correlates with the performance improvements
reported.
• INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS”. NO LICENSE, EXPRESS OR IMPLIED, BY
ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS
DOCUMENT. INTEL ASSUMES NO LIABILITY WHATSOEVER AND INTEL DISCLAIMS ANY EXPRESS OR
IMPLIED WARRANTY, RELATING TO THIS INFORMATION INCLUDING LIABILITY OR WARRANTIES RELATING
TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT,
COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT.
• Performance tests and ratings are measured using specific computer systems and/or components and reflect the
approximate performance of Intel products as measured by those tests. Any difference in system hardware or
software design or configuration may affect actual performance. Buyers should consult other sources of information
to evaluate the performance of systems or components they are considering purchasing. For more information on
performance tests and on the performance of Intel products, reference www.intel.com/software/products.

IBM’s statements regarding its plans, directions, and intent are subject to change or
withdrawal without notice at IBM’s sole discretion.
Information regarding potential future products is intended to outline our general
product direction and it should not be relied on in making a purchasing decision.
The information mentioned regarding potential future products is not a commitment,
promise, or legal obligation to deliver any material, code or functionality. Information
about potential future products may not be incorporated into any contract. The
development, release, and timing of any future features or functionality described for
our products remains at our sole discretion.
Please Note:
Performance is based on measurements and projections using standard IBM
benchmarks in a controlled environment. The actual throughput or performance that
any user will experience will vary depending upon many factors, including
considerations such as the amount of multiprogramming in the user's job stream, the
I/O configuration, the storage configuration, and the workload processed. Therefore,
no assurance can be given that an individual user will achieve results similar to those
stated here.

04/23/13 57
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presentation or any other materials. Nothing contained in this presentation is intended
to, nor shall have the effect of, creating any warranties or representations from IBM or
its suppliers or licensors, or altering the terms and conditions of the applicable license
agreement governing the use of IBM software.
Acknowledgements and
Disclaimers:

Acknowledgements &
Disclaimers:
© Copyright IBM Corporation 2013. All rights reserved.
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restricted by GSA ADP Schedule Contract with IBM Corp.
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