From Linux Foundation Collaboration Summit 2015, as presented by Larry Wikelius of Cavium Inc on 2015 02-18. ARM-powered servers for cloud computing and data center servers took a big step forward when Cavium recently announced availability of the industry’s first 48-core family of ARMv8 workload optimized processors. Delivering the highest core counts and most comprehensive set of I/O and accelerators in the market, ThunderX-based solutions are fully optimized for a targeted set of highly scalable workloads. In his talk, Larry Wikelius, who spearheads ecosystems and partner enablement for Cavium, will discuss growing momentum for ARMv8-based servers for hyperscale data center, cloud servers, big data and scale out computing as well as share new performance benchmark data. In addition, developments in the networking and carrier space are quickly mobilizing behind programs like The Open NFV Organization. The Linux Foundation, Xen Project, Open Compute Project and Linaro are also key.

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A Look Ahead: ARM Servers for Next-Generation
Data Center & Cloud Infrastructure

2. Copyright Cavium 2015
Changing the Computing Landscape With ARM

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Connectivity is in Hyper Growth Stage

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61%
39%
49%
51%
37%
63%
201720162015201420132012
0
20
40
60
80
100
120
140
160
180
200
Installed Servers in
Cloud in Millions
CLOUDDATACENTER
(30% CAGR)
TRADITIONAL DATA CENTER
(6% CAGR)
World is Increasingly Moving To The Cloud

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2000 2005
2.8 ZB
72 ZB/yr
2014
Complexity increases exponentially with scale
Rapidly Expanding Physical Infrastructure

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Virtual Platform
• Cloud
Compute/Storage
• Network
Virtualization (NFV)
• SDN
Server Market Faces Disruption
Public Clouds &
Enterprise Servers
• 20% inc in shipment
in 2014
• Cloud driving
growth in server
shipments
ODM Direct &
High Density Server
• Direct ODM model –
OpenCompute
• Scale out models
Workload
Optimized Servers
• Targeted Application
• Optimized for
performance

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of
DIVISION
LABOR

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Multiple applications consolidated
in MULTI-TENANT SERVER FARM
ONE APPLICATION
used by 10M+ USERS
The Drive for Workload Optimization

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TRADITIONAL
One Size Fits All Model.
LegacyArchitectures are
stuckinthepast Focusedonlyaround
CPU&memory
Lackofintegrationof
WorkloadOptimization
WORKLOAD OPTIMIZED
STORAGE NETWORK SECURITYCOMPUTE
OPTIMIZED for
COMPUTING
48
CORES
NETWORKING
MEMORY MEMORY

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• Single threaded or limited multi-threaded
program(s)
• Structure query Databases / Email / Compilers/Interpreters
• Workload performance primarily dependent on
CPU/memory performance
• 1000s of applications used by 1000s of users
• Virtualization used to improve server utilization
• Managed by traditional IT
• Evaluated by traditional benchmarks
Legacy Server Applications

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• Highly Distributed – the “System(s) are the Computer”
Shared-nothing architectures - Distributed Data & Distributed Compute
Many node environments
Highly parallel – add more threads, go faster
Multiple OS instances – fault tolerance in SW
• Big Data
Large and highly distributed data sets
• Nodes are often special purpose
Cloud Applications can benefit from a “New Class of Servers”
New class of servers requires new class of benchmarks
Cloud Applications = New Paradigm Of Computing

12. Copyright Cavium 2015
Workload Example/Use Case
Graph Search Social media data analysis (e.g. GraphLab, Giraph)
Web Caching Memcached
Media Serving Video server – e.g. “DASH” servers
Web Serving LAMP + Java/Tomcat/Ruby…
Data Analytics Hadoop (Mahout, Nutch)
Distributed Search Elastic Search
Distributed Storage Ceph (Object/Block) and HDFS (File)
Data Serving NoSQL type databases (e.g. Cassandra, Hbase, …)
Example Cloud Workloads

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0
20
40
60
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120
140
160
data
caching
data
serving
map
reduce
media
streaming
web front
end
web
search
specint tpc-c tpc-e
SpecINT2006
Scaleout workloads
Source data from : A Case for Specialized Processors for Scale-Out Workloads Michael Ferdman, Almutaz Adileh, Onur Kocberber, Stavros Volos, Mohammad Alisafaee, Djordje
Jevdjic, Cansu Kaynak, Adrian Daniel Popescu, Anastasia Ailamaki, Babak Falsafi, In IEEE Micro's Top Picks, 2014
Cloud Workloads are Different
Example #1 – Very different instruction miss rates
MissesPer1KInstructions(MPKI)
MPKI

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0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cloud Workloads are Different
Example #2 – impact of IPC changing
InstructionPerCycle(ipc)
CPU Intensive
Traditional Benchmarks
Source data from : A Case for Specialized Processors for Scale-Out Workloads Michael Ferdman, Almutaz Adileh, Onur Kocberber, Stavros Volos, Mohammad Alisafaee, Djordje
Jevdjic, Cansu Kaynak, Adrian Daniel Popescu, Anastasia Ailamaki, Babak Falsafi, In IEEE Micro's Top Picks, 2014

15. Copyright Cavium 2015
Cloud Workloads are Different
Example #3 – Performance Sensitivity LLC & L2 Caches
Source data from : A Case for Specialized Processors for Scale-Out Workloads Michael Ferdman, Almutaz Adileh, Onur Kocberber, Stavros Volos, Mohammad Alisafaee, Djordje
Jevdjic, Cansu Kaynak, Adrian Daniel Popescu, Anastasia Ailamaki, Babak Falsafi, In IEEE Micro's Top Picks, 2014

16. Copyright Cavium 2015
• Optimum Choice and size of Caches are different for scale out workloads
• Lower IPC
Less parallelism available
Aggressive, out-of-order, wide issue machines don’t benefit
Don’t waste area and power
• Scaleout
Highly parallel nature, more independent processing cores.
Use the area for more cores
Large number of more efficient cores provide lower power and more
performance for Scale Out Workloads
Implications to Processor Design

17. Copyright Cavium 2015
Can fit multiple cores in area of one complex core
For Scale Out workloads, multiple cores provide the best
performance/unit area / watt
Complex Single Core Multiple Cores
Drive to Efficiency

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AArch64 – ARMv8

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•ARM designs and licenses IP
Standard License
Architectural License
•ARM enables ecosystem via
Foundation Models
Reference Systems
•ARM designs and sells tools and supporting collateral
ARM Engineering Model

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• Focused on CPU and memory (scalar performance)
• Very little network or disk IO
• Not a distributed application
• No cooperative tasks, no sharing, no communication
• No operating system or Hypervisor impact
• Many of the sub elements are cache friendly
• Could be very expensive to setup and run
• Some need very big systems
Characteristics of Traditional Benchmarks
Traditional Benchmarks do not adequately represent Cloud Workloads

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Workload Focused
Support large & distributed Data Sets
Scalable across many nodes & cores
Different Behavior from Transaction & CPU Memory
SoC includes more “System” features
Need to expand beyond CPU & memory and include network & storage IO
New Scale Out Benchmarks

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open source
open source
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open source
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OPEN
SOURCE
OPEN LICENSES
OPENINNOVATION
OPENDISTRIBUTION
OPENCOMPETITION
OPENPARTNERSHIP
OPENDATASTANDARDS
OPENOPPORTUNITIES
OPENTECHNOLOGIES
Open Source is Expanding

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Standards – Core to Innovation and Scale
The broader industry committed and engaged

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● Formed in November 2012
● Working on core open-source
software for ARM servers
● Boot architecture – UEFI/ACPI
● Virtualization – KVM/Xen
● ARMv8 bringup & optimization
● LAMP, OpenJDK, Hadoop, OpenStack
● Reduces costs, eliminates
fragmentation, accelerates time
to market
● Members can focus on
innovation and differentiated
value-add
Linaro Enterprise Group (LEG)
http://wiki.linaro.org/LEG

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Linux Foundation

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Leading System Vendors

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Commercial Software Vendors

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Summary
Infrastructure HyperGrowth
Server Disruption
Workload Optimization
Rapidly Growing EcoSystem

29. Copyright 2014 Cavium. Confidential.