This session will begin with an overview of current non-volatile memory (NVM, aka persistent memory) architectures and its relationship between several levels of memory and storage hierarchy, both near- and far-processor. A discussion on its significant impact on computing analytic workloads now and in the near future will ensue, including use cases and the concept of very large persistent memory surfaces as applied to both analytic computation and storage for big data workflows. The presentation will end with ‘why you should care’ about such technologies which inevitably will completely change the way we think about solving data-intensive problems.

1. 1 | ©2015 Micron Technology, Inc.July 17, 2015
Rob Peglar
VP, Advanced Storage, SBU
@peglarr
Analytics, Big Data,
Non-Volatile Memory,
and Why You Should Care

2. The Present - Architecture
BUSINESS PROCESSINFO PROCESSINGDATA ACQUISITIONDATA CREATION
END USERSANALYSTS / SCIENTISTSARCHITECTS / ENGINEERSPRODUCERS
Shared Nothing
Scale-out Storage + SSD
MPP + In-Memory
Compute
Hadoop
Hi-Speed / -
Resiliency
Networking
Converged
Infrastructure
Cloud
Non-relational
DWH
SYSTEMS INTEGRATION
VOLUMEVELOCITYVARIETY
OBJECTIVES
Stream Processing
Event Management
Data Exploration
Contextualized Data
Modeling / Scenarios
Forecasting
DELIVERY MODELS
Access-Anywhere
Analytics Services
Context-Aware
Business Applications
ON-DEMAND
Location-Based
Services
Alert and
Respond
PUSH
Workflow and
Interaction
Automation
Smart devices
and systems
EMBEDDED
Email and
Messaging
Mobile Apps Data
Transaction and
Usage Logs
Machine and
Sensors
Geolocation
Relationships and
Social Influence
Real-time
Events
Deep
Insights
VALUE
July 17, 2015 | ©2015 Micron Technology, Inc.2

3. The Future – N=all? Keep
Everything? Seriously?
Data Silos or the Data Lake?
HDFS presents a crisis: i.e. 危機, weiji
dangerous ‘critical point’ (not opportunity; mis-translation)
Write-once, read-many, modify-never; delete-never?
Time is not your friend when moving data
(So, don’t move it between repositories; move it to the CPU)
One 40GE NIC yields same rate on bus as 28 HDD @ 140MB/s
One million seconds is 277.7 hours (~ 11.5 days)
1 PB @ 1 GB/sec is … 1 EB @ 1 TB/sec is …
Non-shared (1 protocol) or shared (N protocols)?
Time versus Space – the Essential Judgment
Cost of Having Data vs. Cost of Not Having Data
July 17, 2015 | ©2015 Micron Technology, Inc.3

4. Computing Trends
aka Stuff you Cannot Ignore
July 17, 2015 | ©2015 Micron Technology, Inc. |

5. The Macro Trend – Back to the Future
In 1984 John Gage of Sun Microsystems said:
“The Network is the Computer”
Personal Note – I was working with Sun workstations in 1984, and
appreciated what he said – he was right
Back then we had compute & storage in a ‘workstation’
Which we now know as a server
Everything was local – no SAN, no RDMA, no shared storage
Sun’s innovation was to put LAN interfaces – 10Mb/sec
Ethernet – inside the workstation – as standard
The entire design of SunOS revolved around LAN
connections
Today, “the Server is the Computer” – back to the future
Hyperscale – Hyperconvergence – Hyperclustering –
Hyperdistributed - Hyperhype?
No…it really is one of those once-in-a-generation inflection points!
July 17, 2015 | ©2015 Micron Technology, Inc. |

6. Trend #1 – Software-Defined Everything
Software-Defined is a trend to further abstract low layers of
the ‘stack’ away and collapse all resources into host-based
entities
Simplifies deployment and allows high levels of automation
RESTful APIs FTW – simple automation, scripting,
networking
SD Storage (SDS) is not new – been around since ~1999
Early players did local HDD and SAN array aggregation
Today local SSD, local HDD and SAN/NAS/Object array abstraction
Leverages powerful compute cores & high-bandwidth LANs
Hypervisor-based and bare-metal based – both kinds exist
SD Networking (SDN) is newer
Leverage host-based switching/routing s/w stacks
Use server-based NICs like switch-based ports
QoS, out-of-band, other operations much easier to abstract with SDN
Mostly hypervisor-based today (e.g. VMW NSX)
July 17, 2015 | ©2015 Micron Technology, Inc.
6

7. Trend #2 – The Start of the End of SAN
Local (host-based) storage is once again rising – especially SSD
PCI-E/NVM-E – extremely high throughput/low latency storage
SAN was useful for sharing storage capacity and aggregating
storage provisioning/assigning/control paths
The logical extension of multi-initiator SCSI in the late ‘80s/early ‘90s
Today SDS and host-based clustered file and object systems
perform the same functions w/o array controllers in the control
& data paths
Moving away from host-fabric-array configurations using
channel technology (e.g. FC, SAS)
Moving (back) towards host-host configurations using LAN
technology (e.g. Ethernet, IB)
Commoditize, standardize, virtualize, containerize, automate
July 17, 2015 | ©2015 Micron Technology, Inc. |

8. Trend #3 – The Start of the End of HDD
The HDD has been with us since 1956
IBM RAMAC Model 305 (picture )
50 dual-side platters, 1,200 RPM, 100 Kb/sec
5 million 6-bit characters (3MB)
Today – the SATA HDD of 2015
7 dual-side platters, 7,200 RPM, 100 MB/sec
8 trillion 8-bit characters (8TB) in 3.5”
Over 2 million X denser and 10,000 X faster (throughput)
Problem is only 6X faster rotation speed – which means latency
With 3D TLC NAND technology we can easily build 10TB 2.5”
SSDs
Which means we’ve solved the capacity/density problem while
the throughput & latency problem was already solved
And continues to improve by leaps and bounds (e.g. NVM-E)
On a $/TB basis we are nearing parity as deployed in servers
July 17, 2015 | ©2015 Micron Technology, Inc. |
8

9. 9 | ©2015 Micron Technology, Inc. l
0%
25%
50%
75%
100%
Enterprise DRAM Market Density Transitions
Source: Micron Business Development - 4Q14
DRAM %
Equivalents
1Gb DDR2
1Gb DDR3
2Gb DDR3
4Gb DDR3
8Gb DDR4
4Gb DDR4
2Gb DDR2
2014 2015 2016 2017 2018
4way+ High End
-1600/-1866
1.35V/1.5V
-1866/-2133
1.5V/1.2V
-2400
1.2V
-2400
1.2V
-2400
1.2V
1way and 2way
-1866/-2133
1.5V/1.2V
-1866/-2400
1.2V
-2400/-2666
1.2V
-2666/2933*
1.2V
-2933*/2933*
1.2V
Technology DDR3L/DDR4 DDR3L/DDR4 DDR4 DDR4 DDR4
8Gb DDR3
16Gb DDR4

10. 10 | ©2015 Micron Technology, Inc. l
-10%
0%
10%
20%
30%
CQ4'14 CQ1'15 CQ2'15 CQ3'15 CQ4'15 CQ1'16 CQ2'16 CQ3'16 CQ4'16
DDR4 Highlights
Key Points
Module details
 Speed at introduction - 2133
 2DPC loading required
 x4 vs x8 mix is ~80/20
Transitions (workload
dependent)
 RDIMM moves to LRDIMM
 Single load for multiple DPC
 LR buffer more economical
than 3DS/TSV
DDP moves to 3DS/TSV
 Standard DDP achieves 2133
speed
 2H 3DS required for 2400 and
above
DDR4 Versus DDR3 Price Premium
Market Trends
0%
25%
50%
75%
100%
1H'14 2H'14 1H'15 2H'15 1H'16 2H'16 1H'17 2H'17
8GB RDIMM
16GB RDIMM
32GB LRDIMM
32GB RDIMM
64GB LRDIMM
Other
Price parity expected in
4Q’15

11. 11 | ©2015 Micron Technology, Inc. lJuly 17, 2015
+Flash and Trash
+Predicted in 2008
+Now coming true
+Memory is Storage
+Storage is Memory
+Von Neumann
+ in Reverse
+CPU is peripheral
+Peripheral is central
Ground
Select
Transistor
Word
Line 0
Word
Line 1
Word
Line 2
Word
Line 3
Word
Line 4
Word
Line 5
Word
Line 6
Word
Line 7
Bit Line
Select
Transistor
Bit Line
P
N N N N N N N N N N N
NAND Flash...and where it leads

12. 12 | ©2015 Micron Technology, Inc. l
What is 3D NAND?
July 17, 2015
3D NAND cells are built
vertically – like a skyscraperGround
Select
Transistor
Word
Line0
Word
Line1
Word
Line2
Word
Line3
Word
Line4
Word
Line5
Word
Line6
Word
Line7
BitLine
Select
Transistor
BitLine
P
NNNNNNNNNNN

13. 13 | ©2014 Micron Technology, Inc. |
A Disruptive Storage Architecture
256 Gb
MLC
384 Gb
TLC
CAPACITY Enables the highest-density flash device ever developed
COST Architected to achieve better cost efficiency than 2D NAND
CONFIDENCE 3D architecture increases performance and endurance

14. 14 | ©2015 Micron Technology, Inc. lJuly 17, 2015
NVM Express – Accessing Flash at High Bandwidth
Courtesy NVMe 1.2 Spec
• NAND Flash via NVMe
• Multiple Namespaces
• Multiple ports/NS
• NS can span Controllers
• 3 GB/sec SSD writes
• 6 GB/sec SSD reads
• PCI-E Gen4 upcoming
• Outstanding xport for
3D NAND devices

15. 15 | ©2015 Micron Technology, Inc. l
Value Proposition
• DRAM-like performance with higher density
and lower energy
• Non-volatility with fraction of DRAM cost/bit
• Ideal for large memory systems such as in-
memory-database/in-memory-compute
• Multiple technologies currently in
industry development and showing
promise
• Controller technology is critical to exploit
characteristics of each type of memory
• Software capable of taking advantage of
the persistent memory semantics
Latency
DRAM NAND
Performance
Focused
Cost
Focused
Endurance
Volatility
Cost
New Memory
Low High
July 17, 2015
Next Steps
NVM – Storage Class Memory – Why you Should Care
• Think about how apps and OS stacks work
• Filesystems exist to persist objects across
reboots – ‘sync’ and HDDs
• NVM changes that model/method
• NVM can act as memory (load/store)
• NVM can act as storage (read/write)
• Pushing the boundaries of x64 physical 48-
bit addressing (256 TB)

16. 16 | ©2014 Micron Technology, Inc. |
Your Next Analytics Compute Engine?
July 17, 2015

17. 17 | ©2015 Micron Technology, Inc. l
Value Proposition
• 5x the bandwidth of DRAM (DDR4) / 16GB space as
implemented in Knights Landing
• Three addressing modes:
• L3 Cache mode (or Hybrid, ‘split’)
• Flat addressing mode
• Ideal for throughput-intense applications using
parallel techniques
• Investigate use of Knights Landing
motherboard designs (e.g. Adams Pass)
• Consider which addressing mode your
applications can optimize around
• Bend your mind into a new memory
hierarchy – ‘near’ and ‘far’ memory
July 17, 2015
Next Steps
MCDRAM – Memory Channel DRAM
Why you Should Care
• Think about how apps and OS stacks work
• DRAM on DIMM has been the volatile
memory technology of choice for decades
• L3 Cache mode gives indirect control over
data placement
• Flat mode gives direct control over data
placement (think: memory mapping)
• 400 GB/sec total throughput (DDR4: 90)

18. 18 | ©2015 Micron Technology, Inc.July 17, 2015
Rob Peglar
VP, Advanced Storage, SBU
@peglarr
Analytics, Big Data,
Non-Volatile Memory,
and Why You Should Care