Advances in Flash Memory Technology & System … · YCSB Benchmark set-up

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Dr. John R. Busch

Advances in Flash Memory Technology & System Architecture to Achieve Savings in Data Center Power and TCO

Vice President and Senior Fellow October 18, 2013

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Forward-Looking Statements

During our meeting today we may make forward-looking statements.

Any statement that refers to expectations, projections or other characterizations of future events or circumstances

is a forward-looking statement, including those relating to market position, market growth, product sales, industry

trends, supply chain, future memory technology, production capacity, production costs, technology transitions and

future products. This presentation contains information from third parties, which reflect their projections as of the

date of issuance.

Actual results may differ materially from those expressed in these forward-looking statements due to factors

detailed under the caption “Risk Factors” and elsewhere in the documents we file from time to time with the SEC,

including our annual and quarterly reports.

We undertake no obligation to update these forward-looking statements, which speak only as of the date hereof.

Cornell University – October 18, 2013

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Overview

About SanDisk

Flash Trends

Flash Optimized Data Center Solutions

Conclusions


4

About SanDisk

5

A Global Leader in Flash Memory Storage Solutions

Financials as of Q2, ‘13. Net Cash = [Cash + cash equivalents + short-term & long-term marketable securities] less [debt at maturity value] as of the end of Q2, ‘13. Headcount & patents as of Aug., ‘13. NPD Estimate, Jan., ‘13. Estimates of the memory card & USB markets from NPD (Jan. ‘13) and GfK Retail and Technology, Oct., ‘12. Gartner: NAND Flash Supply & Demand, WW 1Q ‘12-4Q ‘14, 2Q ’13. Update Jun., ‘13.

The Leading Retail Brand in Key Markets

Close to Half of Industry Bit Output Together with manufacturing partner Toshiba

Technology Leadership

4,900+ Patents

1991 2013

Enterprise SSDs and Storage Software

Qualified at 6 of the Top 7

Server & Storage OEMs

All leading smartphone & tablet manufacturers use SanDisk

SanDisk Client SSD Design Wins at

11 Leading PC OEMs

The Leading Retail Brand in Key Markets

#1 Global Retail Revenue Share

Rankings Trailing 4 Qtr. Financials Global Operations Technology

5,000+ Employees

Fabs World Class NAND Capacity

19nm Leading Process Node

1Ynm Shipping

$5.6B Revenue

$4.4B Net Cash

$0.7B R&D Investment


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Complete Top to Bottom Integration

Full Stack Enables Segment Optimized Solutions

Performance Scalability System

Utilization Endurance Cost Life Cycle

CONTROLLER NAND TECH NAND DIE WAFER SCALE MFG SSD SOFTWARE


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Flash Trends

8

Bits per cell

Toshiba-SanDisk

~4F2 cell

Toshiba – SanDisk Flash Partnership

3D NAND & BiCS

Physical scaling: 210nm 160 130 90 70 56 43 32 24 19nm

Logical scaling: X1 (SLC) X2 (MLC) X3


BiCS = Bit Cost Scalable

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2D-NAND Scaling Considerations

1Y technology node is 19/19.5 nm a substantial cell area reduction

1Y incorporates several new process modules to improve performance and reliability

1Z will leverage 1Y innovations to scale both X, Y dimensions substantial cell area reduction

19 nm 19/26nm

1Y 19/19.5nm

1Z Note: Diagram not to scale


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3D NAND Technology BiCS – 3D charge trap structure Doesn’t require EUV Regular optical tools Bridge technology to 3D

ReRAM

Bit Lines

Source Lines

Select Gates

Word Lines

Back Gate

Y-cut: WL

X-cut: BL

Note: Diagram not to scale

3D NAND--Alternatives to Planar NAND


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3D Resitor RAM : will follow 3D-NAND BiCS

3D ReRAM can scale to below 10nm node providing cost reduction beyond 2020


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Spectrum of Memory Technologies


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Positioning/Prospects of Memories

Write/Program Cycle Time (s)

Capa

city

(bits

)

1E-9 1E-8 1E-7 1E-6 1E-5 1E-4 1E-3 1E-2

1T

100G

10G

1G

100M

10M

1M

SRAM

DRAM

MRAM

PCM NOR

FG-NAND

BiCS

Code Storage

HDD

Working Memory

STT

ReRAM

Data Storage

ReRAM and BiCS are the two most promising post-2D NAND candidates

Progression of Memory Technologies


Source: SanDisk, presented at IMEC 2011

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Flash Optimized Data Center Solutions

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Flash is Enabling New Applications, Growing Fast

Source: Gartner

$24.4

$32.9

$13.2

$26.2

$38.7

$23.6

$28.5

$40.2 $38.3

$0

$10

$20

$30

$40

$50

DRAM HDD NAND

2008 2012 2016E

TAM ($B)


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Flash Drives Savings in the Enterprise

Server/SAN Consolidation Hot, warm, and cold data Lower Cost SSD

Tota

l Co

st o

f O

wn

ersh

ip (

$)

High performance HDD

MLC Caching

In-Memory Computing Flash DIMM

3D Capacity HDD

SLC

Time


Cold Storage

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Software Unlocks Flash Potential in the Enterprise Data Center

FLASH + SOFTWARE

New SAN Architecture

In-Memory Databases

Cold Storage

Big Data Analytics

Virtualization & Cloud Computing

Server-side Caching

Flash as replacement for 15k RPM HDD

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Flash-Optimized Applications

Flash-optimized applications: – Exploit the high capacity, low latency, persistence and high throughput of flash memory

– Have extensive parallelism to enable many concurrent flash accesses for high throughput

– Use DRAM as a cache

– Get in-DRAM performance at in-flash capacity and cost, enabling server consolidation

Many applications realize limited benefits from flash without system level optimization

SanDisk ‘Flash Data Fabric (FDF)’ is a substrate for flash-optimized applications – Caching, key-value stores, databases, message queues, custom apps

– Leverages flash for high performance, high availability

– Enables low TCO through high server consolidation

– Executes on bare metal or virtualized


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SanDisk ‘Flash Data Fabric (FDF)’ Enables Direct Flash Access for In-Memory performance

Hardware

Firmware

Driver

Operating System

Middleware

Applications

• Optimizes to fully exploit flash and multi-core

• Encapsulates optimizations for use by any application

• Open and standard initiative

Flash Data Fabric (FDF)


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‘Flash Data Fabric’ features Provides an object API: create, replace, update, delete, indexes, range queries,

transactions, snapshot

Provides multiple namespaces via containers

Maps object keys to flash locations

Intelligent granular DRAM caching

Heavily optimized access paths for high performance

Optimized threading to maximize concurrency and minimize response time

Configurable flash management algorithms to optimize different workloads

Integrates with flash devices

– Minimizes write amplification, fast persistence, vectored operations,…

Executes in user space and is linked in as a dynamically loaded library


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‘Flash Data Fabric’ Architecture

Container Mgmt Naming, create, open, delete

FDF Protocol Layer

Object Mgmt Naming, create, search,

update, delete,

Cluster Mgmt Naming, configure

Local DRAM

Caching

Flash Manager

Replication Elasticity Module

Messaging Subsystem Connect, send, receive

Transport Layer

Threading Module

Databases/Data Stores Data Grid and Object Stores

… Message Queue … … Session Store … Custom Apps

Cluster Services

Rep

licat

ion

Co

nfi

g

Failu

re H

and

ling

Fau

lt D

etec

tio

n

Application Layer


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No SQL DataBase Example : Cassandra

Cassandra is an open source distributed key-value store

Key features:

– support for large scale synchronous and asynchronous replication, including across data centers

– automatic fault-tolerance and scaling

– tunable consistency (from “writes never fail” to “block for all replicas to be readable”)

– efficient support for large rows (1000’s of columns)

– CQL (SQL-like) query language

– supports multiple indices

FDF-Cassandra prototype based on Cassandra 2.1.4


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Cassandra Performance 95/5 workload Stock Cassandra FDF Cassandra

Hard Drives 1.2k tps 100% HDD utilization 1 of 16 cores utilization

N/A

64GB Data (fits in memory)

40K tps 12 of 24 cores utilization

124K tps 18 of 24 cores utilization

256GB Data (data set in flash) 25K tps 90% flash utilization 18 of 24 cores utilization

95K tps 90% flash utilization 19 of 24 cores utilization

Intel Westmere server with 2 x 2.9GHz sockets, 24 cores, 96G DRAM

SSD: 8 x 200G SSD with software RAID 0

YCSB Benchmark set-up Remote client with 10G network connection

1K fixed object, uniform distribution with configurable read/write mix (eg: 95% read, 5 % update)

48GB FDF DRAM cache


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TCO : Cassandra Requirement : 80k TPS and 1 TByte data set

$378,216

$55,620

$ 14,124

$10,000

$100,000

$1,000,000

Stock Cassandra on HDD stock Cassandra in DRAM FDF-Cassandra and Flash

TCO - Log Scale

3 Year OpEx

3 Year CapEx


Source: Based on internal testing

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HDD DRAM SanDisk

No. of servers 34 6 1

Power (kW) 12.7 2.8 0.4

$ per transaction $8.44 $2.49 $0.51

2.4

80

95

0

10

20

30

40

50

60

70

80

90

100

HDD DRAM SanDisk

Tran

saconspersecond(Th

ousand)

Capacity

Performance

FDF + Flash Accelerates Database Performance at a Dramatically Lower TCO

Servers needed for 3.1TB dataset Yahoo! Cloud Serving Benchmark

95% Read 5% Write

Measurements on identical commodity x86 servers; scaled with modeling from 1.0 to 3.1TB Servers are running Apache Cassandra Database Management System


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In Memory Data Grid Example: CouchBase vs FDF-Memcached

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

500000

10

11

0

21

0

31

0

41

0

51

0

61

0

71

0

81

0

91

0

10

10

11

10

12

10

13

10

14

10

15

10

16

10

17

10

18

10

19

10

20

10

21

10

22

10

23

10

24

10

25

10

26

10

27

10

28

10

29

10

30

10

31

10

32

10

33

10

34

10

35

10

TPS

Couchbase

FDF-Memcached


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In Memory Database Example: FDF-Redis Performance

116

84

93

70

93

132

101

114

99

89

0

20

40

60

80

100

120

140

String Hash List Set Sorted Set

KTP

S

Stock Redis (in memory)

FDF-Redis (out of memory)

FDF-Redis throughput with data set in Flash matches

Stock -Redis throughput with data set in DRAM


Bare Metal


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TCO : Stock Redis vs FDF-Redis Requirement : 80k TPS and 1 TByte data set

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Stock-Redis AWS in DRAM FDF-Redis AWS with SSD

3 Year TCO

TCO


$-

$50,000

$100,000

$150,000

$200,000

$250,000

Stock Redis with DRAM96GB Servers

FDF-Redis with Flash

3 Year OpEx

3 Year CapEx

Bare Metal 3 Year TCO AWS


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Cloud Storage Example: FDF-Swift vs Stock-Swift Performance


5.8

93

149

222

2.628

40

80

134

2.635 17.261 19.672 19.493

2.632 9.852 10.431 9.242

0

50

100

150

200

250

1 8 16 32 64

TPS

in K

s

Clients

FDFSwift In-memory FDFSwift in-flash StockSwift in-memory StockSwift in-flash

Source: Based on internal testing, October 2013

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Client Number

TPS in Ks FDF Object

server CPU

DRAM Miss rate

IO Utilization

1 2.6 95% 30% 5%

16 40 430% 58% 48%

32 80 1048% 58% 75%

64 134 2094% 58% 92%

Client # TPS in Ks Stock Object server CPU

IO Utilization

1 2 1000% 15%

8 9 2400% 45%

16 10 2400% 45%

32 9 2500% 45%

Stock-Swift in Flash

FDF-Swift in Flash


Source: Based on internal testing, October 2013

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Conclusions

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Conclusions

Flash technology trends will reduce TCO below all competing storage technologies

Many applications realize limited benefits from flash without optimization

Flash optimization of applications can yield near in-DRAM performance with balanced server with datasets residing in flash

Broad new set of flash use cases covering entire data center: hot to warm to cold data

,Cornell University – October 18, 2013

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Thank you! © 2013 SanDisk Corporation. All rights reserved. SanDisk, SanDisk Ultra, SanDisk Extreme and SanDisk Extreme Pro are trademarks of SanDisk Corporation, registered in the United States and other countries. Lightning

is a U.S. registered trademark of SanDisk Enterprise IP LLC. iNAND Extreme is a trademark of SanDisk Corporation. ULLtraDIMM is a trademark of SanDisk Enterprise IP LLC. The SD and the SDHC mark and logo are

trademarks of SD-3C, LLC. Other brand names mentioned herein are for identification purposes only and may be the trademarks of their respective holder(s).

1GB=1,000,000,000 bytes. Actual user capacity less.

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Advances in Flash Memory Technology & System … · YCSB Benchmark set-up