LF Collab Summit 2015: ARM Servers for the Next Generation Date Center and Cloud Infrastructure

Copyright Cavium 2015

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


Changing the Computing Landscape With ARM


Connectivity is in Hyper Growth Stage


61%

39%

49%

51%

37%

63%

2017 2016 2015 2014 2013 2012 0

20

40

60

80

100

120

140

160

180

200

Installed Servers in Cloud in Millions

CLOUD DATA CENTER (30% CAGR)

TRADITIONAL DATA CENTER (6% CAGR)

World is Increasingly Moving To The Cloud


2000 2005

2.8 ZB

72 ZB/yr

2014

Complexity increases exponentially with scale

Rapidly Expanding Physical Infrastructure


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


of DIVISION

LABOR


Multiple applications consolidated in MULTI-TENANT SERVER FARM

ONE APPLICATION used by 10M+ USERS

The Drive for Workload Optimization


TRADITIONAL One Size Fits All Model.

Legacy Architectures are

stuck in the past Focused only around

CPU & memory

Lack of integration of

Workload Optimization

WORKLOAD OPTIMIZED

STORAGE NETWORK SECURITY COMPUTE

OPTIMIZED for COMPUTING

48 CORES

NETWORKING

MEMORY MEMORY


• 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


• 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


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


0

20

40

60

80

100

120

140

160

datacaching

dataserving

mapreduce

mediastreaming

web frontend

websearch

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

Mis

ses

Per 1

K In

stru

ctio

ns (M

PKI)

MPKI


00.20.40.60.8

11.21.41.6

Cloud Workloads are Different Example #2 – impact of IPC changing

Inst

ruct

ion

Per C

ycle

(ipc

)

CPU Intensive Traditional Benchmarks



Cloud Workloads are Different Example #3 – Performance Sensitivity LLC & L2 Caches



• 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


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


AArch64 – ARMv8


• 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


• 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


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


open source

open source

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open source

open source

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open source

open source

open source

open source

open source

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open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

open source

OPEN SOURCE

OPEN LICENSES

OPEN INNOVATION

OPEN DISTRIBUTION

OPEN COMPETITION OPEN PARTNERSHIP

OPEN DATA STANDARDS OPEN OPPORTUNITIES

OPEN TECHNOLOGIES

Open Source is Expanding


Standards – Core to Innovation and Scale

The broader industry committed and engaged


● 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

http://wiki.linaro.org/LNG


Linux Foundation


Leading System Vendors


Commercial Software Vendors


Summary Infrastructure HyperGrowth Server Disruption

Workload Optimization Rapidly Growing EcoSystem

Copyright 2014 Cavium. Confidential.

Technology

LF Collab Summit 2015: ARM Servers for the Next Generation Date Center and Cloud Infrastructure