Document provided by Sheng Xu (IBM China)

1. © 2018 IBM CorporationConfidential
China Systems Lab
Declares Power9 “Google Strong”
• More threads
• More memory bandwidth
• Google Cloud POC started
Hyperscale datacenter provider Tencent says that since
purchasing some OpenPOWER-based systems for its
enterprise datacenter, its efficiency has improved by 30%,
and it saved 30% on both rack resources and server
resources

2. © 2018 IBM CorporationConfidential
China Systems Lab
• China server market continues to grow at 10%+ CAGR…
• While POWER has been steadily declining due to China domestic policy and
technology shift in market
6,459
8,105
9,686
12,099
14,186
16,114 17,448
0
5,000
10,000
15,000
20,000
2014 2015 2016 2017 2018 2019 2020
x86 Power
China Sever Market & Forecast by Segments (w/o MF)
IBM MD&I | 2018 | © IBM Corporation | IBM Confidential
Linux Server Competition: Inspur continued to lead the market and gained share in 2017.
Huawei was #2 but lost share. Dell and H3C saw strong growth
Competition & Ecosystem – Systems
Source: IDC AP Server Tracker 17Q4
Notes: IBM Linux Server saw 364% YTY growth in 2016, which did not reflect in above chart
Inspur 2017
Huawei 2017
Dell 2017
H3C 2017
Lenovo 2017
Sugon 2017
IBM 2017
Inspur 2016
Huawei 2016
Dell 2016
H3C 2016
Lenovo 2016
Sugon 2016
-30%
-10%
10%
30%
50%
70%
0% 5% 10% 15% 20% 25%
IDC China Linux Server Vendor Revenue (US$M)
AnnualGrowthRate
• Bubble Size –Vendor Revenue
• Vertical: Annual Growth Rate
• Horizontal: Market Share
Highlight
• IBM Linux s
YTY growth
$83M reven
market sha
not reflect i
• Huawei’s m
declined fro
19.9% (-0.5
because Hu
focused on
up some w
in 2017
Market Share
• Inspur continues to lead the Linux Server market and is gaining share, esp. the
Internet DC with >27% share
• Huawei & Lenovo’s share all declined as they see profit challenge
• Inspur is also pursuing a strategy to further push share over 50% with major clients
and improve profitability
• China AI/cognitive market will growing with more than 60%
• Growth rate during 2017 to 2020
• AI/cognitive infrastructure market is only one piece of whole
market, pay attention on platform software and related services

3. © 2018 IBM CorporationConfidential
China Systems Lab
• Ultimate pursuit of Cost, Performance, TCO
• De-coupling solutions from vendors
• Big fans of opensource and cutting-edge technology
Designed for Massive Scale Superior TCO for Key Workloads
Open-source firmware
Open Innovation
Leadership performance
Strong ODM partnerships
Database
Search
Analytics
| OpenBMC | OpenPOWEROpen Standards Open CAPI |
Storage Controller
Content Distribution
Artificial Intelligence

4. Power9 Bigdata – Hive+SparkSQL mixed workload
What is it?
4
0 WL M 1P 3H H YRS HK OH M LU L_P
PU JLY HPU YLHS WY K J P U 4U
B WPJHS L JH L.
4U LYWYP L PU LYUHS IP KH H WSH M YT/
S P LUHU KLW OHYPU /
9 I WL HYPL MY T YHUK T PU LYHJ P L
X LY KHPS T U OS YLW Y /
B WPJHS AD HJR. FHYU 7P L AWHYRA ;
B WPJHS LU LYWYP L L JH L IP THYRL /
BYHU WHYLU YLWSHJLTLU M E, P O
S LY B2 M Y IL LY LMMPJPLUJ MSL_PIPSP
4U LYWYP L J TLY P O PU LYUHS IP KH H
WSH M YT WPJHSS DLI' % J TWHUPL
MMLY T YL HYPL PL OH PT SH PU LU LYWYP L
PU LYUHS IP KH H WY K J P U 4U .
S P LY
S P Q I WL b AWHYRA ; S U O Y
S P J TWYL P U WL
S P MPSLG WL
S P 9H HG W P U OLU ITP PU Q I
2 TWH PISL P O E, S P U
0ST YHU WHYLU P JO MY T E,
C HSS U AD W Y PU ULLK
SL_PISL 2 C T S P OYLHK L PU M Y IL 8
HUK LMMPJPLUJ PU KPMMLYLU L JH L .
A B' WYLMLYYLK M Y PTL JYP PJHS Q I
HJOPL L J TWL P P L WLYM YTHUJL H AR SHRL
A B) WYLMLYYLK M Y OY OW MPY Q I
HJOPL L (% HKKP P UHS OY OW /
YLMLYYLK AD LY P U .
W PTPaLK WLU93: W YLHTLK
LJ TWPSLK UH P L SPIYHYPL P O
WLU D4 0K HUJLK B SJOHPU
7HK W ' 'c' c' -
AWHYR & c' '
YL LY P U HUK Q I WL P J TL U.
7HK W ( & AWHYR ' (
A YLHTPU 6YHWOE Q I
C HSS IH LK U WLU A YJL S P U/
8U WYHJ PJL OLYL J SK IL TL O TLTHKL
PTWY LTLU Y W PTPaH P U HYK
TL M OL WLU YJL J TW ULU M Y
IL LY HKHW P U J TLY LU PY UTLU
Y I PUL YLX PYLTLU L FHYU
JOLK SLY
What is the biz value& target markets? Difference with TPC-DS
What is the solution on x86 Advantage on OpenPOWER? Whole solution stack ready on Power?

5. © 2016 IBM Corporation
Power9 Leads Innovation vs. Intel & AMD
Key Performance
Feature
Power9 Scale Out Intel Skylake Gold AMD Epyc
Cores & Threads Up to 22 cores
Up to 88 threads
Up to 22 cores
Up to 44 threads
Up to 32 cores (8x4 NUMA)
Up to 64 threads
L3 Cache Up to 120MB Up to 30.25MB Up to 64MB
TDP / Freq compares 18C 160 W / 2.5 GHz
22C 225 W / 2.9 GHz
18C 160W / 2.7 GHz
22C 165W / 2.2 GHz
32C 180W / 2.2 GHz
Max Boost Freq 3.8 GHz 3.7 GHz 3.2 GHz
Memory Channels /
Bandwidth
Up to 8 channels DDR4
Up to 150 GB/s
Up to 6 channels DDR4
Up to 112 GB/s
Up to 8 channels DDR4 (4x2)
Up to 150 GB/s (4 x 38 GB/s)
PCIe
gen3 = 8 Gb/s; gen4 =
16 Gb/s
42 lanes of PCIe gen4
672 Gb/sec (no NUMA)
48 lanes of PCIe gen3
384 Gb/sec
4 x 32 lanes of PCIe gen3
1024 Gb/sec (4 x 256 Gb NUMA)
OpenCAPI / NVLink
Coherent Accelerator
48 lanes @ 25 Gb/s None None
5

6. © 2018 IBM Corporation
Large HSDC Workloads that run well on Power
Memory lookup (memory bound)
Custom search algorithms
Sparse linear algebra
NoSQL Database (memory bound, network bound)
Graph (Neo4j)
KVS (Redis)
Document (MongoDB)
Column-family (Cassandra)
SQL Database (memory bound, storage bound)
MySQL, Postgres, or custom SQL
AI Inferencing / Training (compute bound -> GPU)
Voice recognition (speech to text)
Image facial recognition
Advanced Image and Video tagging
Autonomous driving
Machine Learning (compute bound -> GPU)
Realtime Data Analytics
Terasort
Spark (memory bound)
Hadoop (network & storage bound)
GPUdb / MapD / Brytlyt (NVLink)
I/O Bound
Content Distribution (Flash to Network)
Storage Controller (Flash to CPU ratio)
Sub-workloads:
Compression
Encryption
Transcode
Erasure (Reed-Solomon)
Message Queue
Blockchain

7. © 2018 IBM CorporationConfidential
China Systems Lab