Deploying Massive Scale Graphs for Realtime Insights

Deploying massive scale for
graphs for realtime insights
B Brech
CTO POWER Solutions
blbrech@us.ibm.com

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© 2016 International Business Machines Corporation
Drive Efficiency
- Time Reduction
- Cost Reduction
- Consistency
Better Insights
- Broader Scope
- Learning Models
- Speed & Accuracy
Better Business
- Innovation
- Customer Care
- Reactivity
Business relies more on Data than ever before

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1990’
s
2020’
s
Video
Text
Exa
Pet
a
Ter
a
Gig
a
DataVolume
2000’
s
2010’
s
Structured data
Audio
Image
Me
d
High
Lo
w
ComputationalNeeds
SophisticationofAnalysis
Expressiveness
Digital Marketing
10+% of video
views
Wide Area Imagery
100’s TB per day72 video
hrs/minute
Media
Source: IBM
Market Insights
based on
composite
sources
Safety / Security
Healthcare
Customer
1B camera phones
1B medical images/yr
10s millions cameras
Enterprise Video
Used by 1/3 of
enterprises
Data Volume
Data Velocity
Data Authenticity
Data Complexity
Data Variability
Data Variety
While Data is Exploding

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Time is Money
and
Insights are critical
Ingest Analyze Act Measure Learn
Optimize
Decision time is shrinking

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Recommendation engines
- used in variety of industries
Network intrusion prevention
Fraud prevention
Financial Services
BioMedical - Genomics
Combination of Scale & Speed is critical
in many use cases
Extreme Scale Example:
- 30TB and growing DB
- 25 BG/s ingress
- over 400K updates / Sec
- 60B+ relationships
- Query Response < 200ms

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DB2 > DB2Blu
SAP > SAP Hana
Oracle > 12C
CICS
EnterpriseDB
Etc..
NoSQLs :
MemCached, REDIS,
NEO4J, CASSANDRA,
MARIA, MONGO,
ORIENT, COUCH,
Etc…
Traditional DBs
going in-memory
Designed as
in-memory repositories
AnalyzeDecision
Innovation
Act
Ingest
But in-memory has some constraints and limits.
Data repositories are changing also

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Built with open innovation to
put your data to work across the enterprise
Designed for
Big Data
Open
Innovation
Platform
Superior Cloud
Economics
IBM POWER8 : Designed for Big Data

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UNSTRUCTURED IN-MEMORY STRUCTURED
Flash for extreme
performance
Massive IO
bandwidth
Continuous
data load
Parallel
processing
Large-scale
memory processing
Optimized for a broad range of big data & analytics workloads:
Processors
flexible, fast execution of
analytics algorithms
Memory
large, fast workspace to
maximize business insight
Cache
ensure continuous data load
for fast responses
4X
threads per core vs. x86
(up to 1536 threads per system)
4X
memory bandwidth vs. x861
(up to 16TB of memory)
4X
more cache vs. x862
(up to 800MB cache per socket)
IBM POWER8 brings performance and scale

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POWER Ecosystem
Designed
for Big Data
Workload
Acceleration
Defined
by Software
Retail Healthcare
Banking Government Telecom
Open and
Collaborative
Technology &
Price/Perf
Leadership
Watson
LinuxHadoop
POWER8
Hypervisor
Virt I/O Server
Shared I/O
Single SMP Hardware System
Built in
Virtualization
Leading
Performance
Processor
Innovation
Streams
Foundations
Suzhou
PowerCore
Technology
Virtualization
Offerings
Key solutions:
+Open Source Tools
+Middleware
+Industry Solutions
+ Social / Mobile / Analytics / Cloud
Hadoop
Spark

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Fundamental forces are accelerating
industry change
IT innovation can no longer
come from just the processor
Solution Innovation and
Acceleration is a key to
the future
Price/Performance
Full system stack innovation
required
Moore’s Law
Technology and
Processors
2000 2020
Firmware / OS
Accelerators
Software
Storage
Network
Full Stack
Acceleratio
n (Lower
is
better)
The OpenPOWER Foundation
is an open ecosystem,
using the
POWER Architecture to serve
the evolving needs of
customers.

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NVLINK
GPUFPGA
Flash NIC
MRAM PCM
Solution Acceleration is a key to the future

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NVLINK
GPU
Flash
Graphics – CAE - EDA
Weather
Defense
Financial Services
Bio-Sciences
General: Compression
Encryption
DataBases: Flash
Finance: Algorithms, Facial
Genomics : Algorithms
Decision
Support
Data
Analytics
Financial
Simulations
Genomic
Analysis
Network
Data Forensics
Facial
Recognition
Solution Acceleration is a key to the future

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IBM Data Engine for NoSQL is an integrated platform for large and fast growing NoSQL data
stores. It builds on the CAPI capability of POWER8 systems and provides super-fast access to
large flash storage capacity. It delivers high speed access to both RAM and flash storage which
can result in significantly lower cost, and higher workload density for NoSQL deployments than a
standard RAM-based system. The solution offers superior performance and price-performance to
scale out x86 server deployments that are either limited in available memory per server or have
flash memory with limited data access latency.
Up to 56TB of extended memory with one POWER8 server + CAPI attach FLASH
Power S822L /
S812L
Flash System 900
Power S822L / S812L / S822 LC
NEW
External Flash Configuration Integrated Flash Configuration
Up to 8TB of super-fast storage tier on one POWER8 server
IBM Data Engine for NoSQL
Cost Savings for In-Memory NoSQL Data Stores

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Identical hardware with 3 different
paths to data
FlashSystem
Conventional
I/O (FC) CAPI - E
IBM POWER S822L
CAPI - I
IBM's CAPI NVMe Flash Accelerator is almost 5X more
efficient in performing IO vs traditional storage.
21%
35%
56%
100%
0%
25%
50%
75%
100%
CAPI NVMe Traditional NVMe Traditional Storage -
Direct IO
Traditional Storage -
Filesystem
RelativeCAPI vs. NVMe Instruction Counts per IO
Kernel Instructions User Instructions
ON
CAPI Unlocks the Next Level
of Performance for Flash

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ON
Efficient IO Enables True Utilization
of Storage Bandwidth
 Under heavy load, IOPs per thread
becomes a critical metric for sustaining
throughput in a storage system. As
throughput increases, more CPU is required
to maintain performance.
 CAPI NVMe flash leverages improved path
length, architectural improvements, and
hardware built-in to POWER8 to greatly-
improve the relative IOPs per CPU thread.
 At high levels of IO (sustained millions of
IOPs), more data can be processed more
efficiently, radically changing the amount of
CPU required to “feed the (IO) beast.”
0.6X
1X
2.6X
3.7X
0%
100%
200%
300%
400%
Fibre Channel NVMe CAPI Fibre Channel CAPI NVMe
AverageRelativeIOPs per CPU Thread
CAPI-accelerated NVMe Flash can issue 3.7X more IOs
per CPU thread than regular NVMe flash.

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Neo4j + IBM POWER8:
Unparalleled Scale and Performance
Neo4j on IBM POWER8
The strength and tooling of Neo4j
The performance of POWER8
The scalability of POWER8 & CAPI
Flash
Unrivaled graph application
scalability and performance
ON

© 2016 IBM Corporation
Real-World mixed graph transaction workload
running Neo4j on POWER8 delivers 1.82X better
performance than Intel Xeon E5-2650 v4 Broadwell
711
390
0
100
200
300
400
500
600
700
800
POWER8 x86
RepresentativemixedworkloadThroughput
IBM Power S822LC (20c/160t) x86 Broadwell Server (24c/48t)
82%
More
Throughput
• POWER8 delivers 1.82X more
query throughput for a
representative mixed sample
workload than x86
– POWER8 (20 cores / 256 GB):
– x86 system with Broadwell
processor (24 cores / 256 GB):
•Based on IBM internal testing of single system and OS image running a real-world mixed graph transaction workload based on LDBC benchmark. Conducted under laboratory condition, individual result can vary based on workload size, use of storage
subsystems & other conditions.
• IBM Power System S822LC; 20 cores (2 x 10c chips) / 160 threads, POWER8; 256 GB memory, Neo4j, Ubuntu 16. Competitive stack: HP Proliant DL380 Gen9; 24 cores (2 x 12c chips) / 48 threads; Intel E5-2650 v4; 256 GB memory, Neo4j, RHEL 7.2 .
Pricing is based bundled pricing for S822LC with Integrated CAPI Flash card.

© 2016 International Business Machines Corporation 18
Scale up and/or out based on your
application requirements
• Out-of-order, super-
scalar design for
exploiting instruction
level parallelization
leading to low CPI
• Larger caches and
99.94% data-cache
hit rate
• SMT design to improve
core efficiency and
increase throughput
capability
Use the paradigm shift to realize your
imagination
CAPI-Flash
Performance and Scale as YOU Need ON

Open innovation to put data to work
across the enterprise
Thanks!
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© Copyright International Business Machines Corporation 2016
Printed in the United States of America September 2016
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Version 1.1
January, 2016

Deploying Massive Scale Graphs for Realtime Insights

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