Warehouse-Scale Computers to Exploit Request-Level and Data-Level Parallelism: .pptx

Copyright © 2012, Elsevier Inc. All rights reserved. 1
Chapter 6
Warehouse-Scale Computers
to Exploit Request-Level and
Data-Level Parallelism:
Computer Architecture
A Quantitative Approach, Fifth Edition

2
Copyright © 2012, Elsevier Inc. All rights reserved.
Introduction
 Warehouse-scale computer (WSC)
 Provides Internet services

Search, social networking, online maps, video sharing, online
shopping, email, cloud computing, etc.
 Differences with HPC “clusters”:

Clusters have higher performance processors and network

Clusters emphasize thread-level parallelism, WSCs
emphasize request-level parallelism
 Differences with datacenters:

Datacenters consolidate different machines and software into
one location

Datacenters emphasize virtual machines and hardware
heterogeneity in order to serve varied customers
Introduction

3
Introduction
 Important design factors for WSC:
 Cost-performance

Small savings add up
 Energy efficiency

Affects power distribution and cooling

Work per joule
 Dependability via redundancy
 Network I/O
 Interactive and batch processing workloads
 Ample computational parallelism is not important

Most jobs are totally independent

“Request-level parallelism”
 Operational costs count

Power consumption is a primary, not secondary, constraint when designing
system
 Scale and its opportunities and problems

Can afford to build customized systems since WSC require volume purchase
Introduction

4
Prgrm’g Models and Workloads
 Batch processing framework: MapReduce
 Map: applies a programmer-supplied function to each
logical input record

Runs on thousands of computers

Provides new set of key-value pairs as intermediate values
 Reduce: collapses values using another
programmer-supplied function
Programming
Models
and
Workloads
for
WSCs

5
 Example:
 map (String key, String value):

// key: document name

// value: document contents

for each word w in value
 EmitIntermediate(w,”1”); // Produce list of all words
 reduce (String key, Iterator values):

// key: a word

// value: a list of counts

int result = 0;

for each v in values:
 result += ParseInt(v); // get integer from key-value pair

Emit(AsString(result));
Programming
Models
and
Workloads
for
WSCs

6
 MapReduce runtime environment schedules
map and reduce task to WSC nodes
 Availability:
 Use replicas of data across different servers
 Use relaxed consistency:

No need for all replicas to always agree
 Workload demands
 Often vary considerably
Programming
Models
and
Workloads
for
WSCs

7
Computer Architecture of WSC
 WSC often use a hierarchy of networks for
interconnection
 Each 19” rack holds 48 1U servers connected
to a rack switch
 Rack switches are uplinked to switch higher
in hierarchy
 Uplink has 48 / n times lower bandwidth, where n
= # of uplink ports

“Oversubscription”
 Goal is to maximize locality of communication
relative to the rack
Computer
Ar4chitecture
of
WSC

8
Storage
 Storage options:
 Use disks inside the servers, or
 Network attached storage through Infiniband
 WSCs generally rely on local disks
 Google File System (GFS) uses local disks and
maintains at least three relicas
Computer
Ar4chitecture
of
WSC

9
Array Switch
 Switch that connects an array of racks
 Array switch should have 10 X the bisection
bandwidth of rack switch
 Cost of n-port switch grows as n2
 Often utilize content addressible memory chips
and FPGAs
Computer
Ar4chitecture
of
WSC

10
WSC Memory Hierarchy
 Servers can access DRAM and disks on other
servers using a NUMA-style interface
Computer
Ar4chitecture
of
WSC

11
Infrastructure and Costs of WSC
 Location of WSC
 Proximity to Internet backbones, electricity cost,
property tax rates, low risk from earthquakes,
floods, and hurricanes
 Power distribution
Physcical
Infrastrcuture
and
Costs
of
WSC

12
 Cooling
 Air conditioning used to cool server room
 64 F – 71 F

Keep temperature higher (closer to 71 F)
 Cooling towers can also be used

Minimum temperature is “wet bulb temperature”
Physcical
Infrastrcuture
and
Costs
of
WSC

13
 Cooling system also uses water (evaporation and
spills)
 E.g. 70,000 to 200,000 gallons per day for an 8 MW facility
 Power cost breakdown:
 Chillers: 30-50% of the power used by the IT equipment
 Air conditioning: 10-20% of the IT power, mostly due to fans
 How man servers can a WSC support?
 Each server:

“Nameplate power rating” gives maximum power consumption

To get actual, measure power under actual workloads
 Oversubscribe cumulative server power by 40%, but
monitor power closely
Physcical
Infrastrcuture
and
Costs
of
WSC

14
Measuring Efficiency of a WSC
 Power Utilization Effectiveness (PEU)
 = Total facility power / IT equipment power
 Median PUE on 2006 study was 1.69
 Performance
 Latency is important metric because it is seen by
users
 Bing study: users will use search less as
response time increases
 Service Level Objectives (SLOs)/Service Level
Agreements (SLAs)

E.g. 99% of requests be below 100 ms
Physcical
Infrastrcuture
and
Costs
of
WSC

15
Cost of a WSC
 Capital expenditures (CAPEX)
 Cost to build a WSC
 Operational expenditures (OPEX)
 Cost to operate a WSC
Physcical
Infrastrcuture
and
Costs
of
WSC

16
Cloud Computing
 WSCs offer economies of scale that cannot
be achieved with a datacenter:
 5.7 times reduction in storage costs
 7.1 times reduction in administrative costs
 7.3 times reduction in networking costs
 This has given rise to cloud services such as
Amazon Web Services

“Utility Computing”

Based on using open source virtual machine and
operating system software
Cloud
Computing

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