HPC Platform and Memory Technologies

Data-CentricPressWorkshop
Move|Store|Process
Dr. Jean-Laurent Philippe
Senior HPC Technical Specialist
Intel Data Center Group Sales
UNDER EMBARGO UNTIL 2 APRIL 2019 AT 10:00 AM (PACIFIC TIME)© COPYRIGHT 2019. INTEL CORPORATION.

90%OFWORLD’SDATAWASCREATED
INTHEPASTTWOYEARS
CLOUDIFICATIONEXPANSION
ANDSCALETOTHEPOINTOFUSE DATA-CENTRICERA
Delivernewservicesfastertohandlethedelugeofdata
CLOUD
COMMS
ENTERPRISE
deliveroperationalefficiencies
Deliversecureinfrastructuretoprotectdataprivacy
2%
ANDONLY
ISBEINGUSED
Source: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/

PERFORMANCETOPROPELINSIGHTS
BUSINESSRESILIENCE
THROUGHHardware-enhancedsecurity
AGILESERVICEDELIVERY
FASTER TIME
TO VALUE
GLOBAL
ECOSYSTEM
WORKLOAD
OPTIMIZED
CUSTOMER
ORIENTED
UNDER EMBARGO UNTIL 2 APRIL 2019 AT 10:00 AM (PACIFIC TIME)
BUILT-IN
VALUE
© COPYRIGHT 2019. INTEL CORPORATION.

STOREMORE PROCESSEVERYTHING
INTEL ATOM® C PROCESSORS
MoveFaster
SILICON PHOTONICS
INTEL® NERVANA™ NNP
SOFTWARE AND
SYSTEM-LEVEL
OPTIMIZED
HARDWARE
ENHANCED
SECURITY
FASTER TIME TO
VALUE
INTEL® MOVIDIUS™ TECHNOLOGY
OMNI-PATH FABRIC
ETHERNET
DC SERIES SSD
Q L C 3 D N A N D D R I V E
INTEL® XEON® D PROCESSORS
INTEL® XEON® SCALABLE PROCESSORS
INTEL® FPGAS

STOREMORE PROCESSEVERYTHING
INTEL ATOM® C PROCESSORS
MoveFaster
SILICON PHOTONICS
INTEL® NERVANA™ NNP
SOFTWARE AND
SYSTEM-LEVEL
OPTIMIZED
HARDWARE
ENHANCED
SECURITY
FASTER TIME TO
VALUE
INTEL® MOVIDIUS™ TECHNOLOGY
OMNI-PATH FABRIC
ETHERNET
DC SERIES SSD
Q L C 3 D N A N D D R I V E
INTEL® XEON® D PROCESSORS
INTEL® XEON® SCALABLE PROCESSORS
INTEL® FPGAS
APRIL 2 ANNOUNCEMENTS

LEADERSHIP WORKLOAD
PERFORMANCE
GROUNDBREAKING
MEMORY INNOVATION
EMBEDDED
ARTIFICIAL INTELLIGENCE
ACCELERATION
ENHANCED
AGILITY & UTILIZATION
HARDWARE ENHANCED
SECURITY
BUILT-IN
VALUE
UNINTERRUPTED
INTEL.COM/XEONSCALABLE

LeadershipXEON
PERFORMANCE
High
Performance
computing
Analytics&
Artificial
intelligence
HIGH
DENSITY
INFRASTRUCTURE
2XMORECOMPUTE
DENSITY
UP
TO112CORES
3.8INTEL®TURBOBOOST
TECHNOLOGY2.0
UP
TO
G
H
Z 3DDR4-2933Mt/S
T
B
UP
TO
UP
TO
2SSYSTEM 2SSYSTEM

2.3 GHz BASE
71.5MB CACHE
48CORES
3.8GHz TURBO
2.6 GHz BASE
77MB CACHE
56CORES
3.8GHz TURBO
2.3 GHz BASE
71.5MB CACHE
32CORES
3.7GHz TURBO
LeadershipXEON
PERFORMANCE
Performance results are based on testing as of dates shown in configuration and may not reflect all publicly available security updates. Configurations and benchmark details can be found on slide/page 50. No product or component
can be absolutely secure. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using
specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully
evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit www.intel.com/benchmarks.
30Xaiperformancewith
intel®dlboost
UP
TO2XAverageperformance
improvement
5.8XBETTERPERFORMANCE
THANAMDEPYC7601
UP
TO
COMPARED TO INTEL® XEON®
PLATINUM 8180 PROCESSOR
HIGHESTDENSITYINTEL®XEON®SCALABLEPROCESSOR
CORESINA2SSYSTEM
HIGHESTDDR4NATIVEBANDWIDTHOFANY
INTEL®XEON®PLATFORM
HIGHESTFLOPSPER2SSYSTEMWITH
INTEL®ARCHITECTURE
COMPARED TO INTEL® XEON® PLATINUM
8180 PROCESSORS (JULY 2017)
COMPARED TO INTEL® XEON® PLATINUM 9282
PROCESSOR RUNNING LINPACK
1 2 3
Intel® Xeon® Platinum 9222 processor: Intel® Turbo Boost Technology frequency and base frequency are preliminary; final values to be published at a future date.

2XSYSTEMMEMORY
CAPACITY
UP
TO8+SOCKETS
4.4INTEL®TURBOBOOST
TECHNOLOGY2.0
UP
TO
G
H
Z 36SYSTEMMEMORY
Inaeightsocketsystem
T
B
UP
TO
&16GbDIMMs
UP
TODDR4
2933MT/s
WORLDRECORDS
ANDCOUNTING…
USING INTEL® OPTANE™ DC
PERSISTENT MEMORY AND DRAM
PLATINUM 8180 PROCESSORINaSYSTEM
95 World Records featuring Intel® Xeon® processors as of September 14, 2018.
https://newsroom.intel.com/news/intel-xeon-scalable-processors-set-95-new-performance-world-records/ INTEL.COM/XEONSCALABLE

14Xaiperformancewith
intel®dlboost
UP
TO
COMPARED TO INTEL® XEON® PLATINUM
8180 PROCESSORS (JULY 2017)
3.1XBETTERPERFORMANCE
THANAMDEPYC7601
UP
TO
COMPARED TO INTEL® XEON® PLATINUM 8282
PROCESSOR RUNNING LINPACK
5-yearrefresh
Performanceimprovement
VM DENSITY COMPARED TO
INTEL® XEON® E5-2600 V2 PROCESSOR
3.50XUP
TO
AVERAGE
PERFORMANCEIMPROVEMENT
GOLD 5100 PROCESSOR
1.33XUP
TO
INTEL®
INFRASTRUCTURE
MANAGEMENT
TECHNOLOGIES
AGILE SERVICE DELIVERY WITH ENHANCED EFFICENCY
MITIGATIONSFOR
SIDE-CHANNEL
methods
Encryption+
Accelerators
BUSINESS RESILIENCE WITH HARDWARE-ENHANCED SECURITY
INTEL®
Security
libraries
Performance results are based on testing as of dates shown in configuration and may not reflect all publicly available security updates. Configurations and benchmark details can be found on slide/page 50 and 51. No product or
component can be absolutely secure. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured
using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully
INTEL®
DEEP
LEARNING
BOOST
INTEL®
Speedselect
TECHNOLOGY
4 5 6 7
Intel® Speed Select Technology is available on select processors.

BusinessresiliencewithHARDWARE-ENHANCEDSECURITY AgileservicedeliverywithenhancedEFFICENCY
INTEL®Securitylibraries
• New Intel® Threat Detection Technology
• Intel® Trusted Execution Technology
• Intel® Cloud Integrity Technology
MITIGATIONSFORSIDE-CHANNELmethods
• Enhanced performance over software-only
mitigations
INTEL®SpeedselectTECHNOLOGY
• Configurable core/frequency processor attributes
• Prioritize workload performance
• Supports platform TCO optimizations
Encryption+Accelerators
• Available with Intel® QuickAssist Technology
• Integrated Intel® Advanced Vector Extensions 512
• Intel® Optane™ DC persistent memory on-module
data encryption
INTEL®INFRASTRUCTUREMANAGEMENTTECHNOLOGIES
• Industry-leading Intel® Virtualization Technology
• Seamless VM migration for over 5 generations
• Enhanced Intel® Resource Director Technology
• New Intel resource orchestration software
• New Intel® Ethernet 800 series with Application Device
Queues (ADQ) and Dynamic Device Personalization (DDP)
• New Intel® Optane™ DC D4800X (Dual Port) SSDs
INTEL®DEEPLEARNINGBOOST
• New integrated AI acceleration for inference

AI.INTEL.COM
Performance results are based on testing as of dates shown in configuration and may not reflect all publicly available security updates. Configurations and benchmark details can be found on slide/page 52. No product or component
can be absolutely secure. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using
specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully
I N T E L O P T I M I Z A T I O N F O R C A F F E R E S N E T - 5 0 N E W A I A C C E L E R A T I O N
INFERENCETHROUGHPUT(IMAGES/SEC)
1.0
5.7X
Jul’17 Dec’18
VECTOR NEURAL NETWORK
INSTRUCTION
ForBUILT-INinferenceAcceleration
OptimizationsforDevelopers&DataScientists
I N T E L ® X E O N ® P L A T I N U M
8 2 0 0 P R O C E S S O R S W I T H
I N T E L ® D L B O O S T
9 2 0 0 P R O C E S S O R S W I T H
I N T E L ® D L B O O S T
Apr’19
Toolkit
optimized
Frameworks
Datatype
8
9
10
11
8 1 0 0 P R O C E S S O R S

Storage
CostperformanceGAp
StoragePerformanceGap
PersistentMemory
Memory DRAM
HOT TIER
HDD / TAPE
COLD TIER
3D NAND SSD
WARM TIER
Improving
SSDperformance
Delivering
efficientstorage
Intel®QLC3DNandSSD
INTEL.COM/OPTANE

BREAK IO BOTTLENECKS
FASTER RECOVERY
HIGH SPEED STORAGE
INCREASE MEMORY SIZE
CONSOLIDATE WORKLOADS
SCALE UP TO SCALE OUT
AFFORDABLE
ALTERNATIVE TO DRAM
IMPROVE TCO
ON-MODULE ENCRYPTION
INTEL.COM/OPTANEDCPERSISTENTMEMORY

Deliver benefits on , to
support to

Intel®Optane™DCPersistentMemoryPerformanceDetails
▪ Intel® Optane™ DC persistent memory is programmable for
different power limits for power/performance optimization
• 12W – 18W, in 0.25 watt granularity - for example: 12.25W, 14.75W, 18W
• Higher power settings give best performance
▪ Performance varies based on traffic pattern
• Contiguous 4 cacheline (256B) granularity vs. single random cacheline (64B) granularity
• Read vs. writes
• Examples:
Granularity Traffic Module Bandwidth
256B (4x64B) Read
256GB, 18W
8.3 GB/s
256B (4x64B) Write 3.0 GB/s
256B (4x64B) 2 Read/1 Write 5.4 GB/s
64B Read 2.13 GB/s
64B Write 0.73 GB/s
64B 2 Read/1 Write 1.35 GB/s

Intel®Optane™DCPersistentMemorylatency
▪ Orders of magnitude lower latency than SSD
▪ 2X read/write bandwidth vs disk, with one module, more with multiple modules
Ranges from 180ns to 340ns
(vs. DRAM ~70ns)
Optane SSD (4KB) (P4800X)
DCPMM (256B)
Read
Read idle latency
Smaller granularity
(vs. 4K)
DCPMM (256B)
Read/Write
NAND SSD (4KB) (P4610)
1. 256B granularity (64B accesses). Note 4K granularity gives about same performance as 256B
Performance results are based on testing as of Feb 22, 2019 and may not reflect all publicly available security updates. No product or component can be absolutely secure.
Results have been estimated based on tests conducted on pre-production systems, and provided to you for informational purposes. Software and workloads used in performance tests may have been optimized for performance only on Intel
microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You
should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more information go to
www.intel.com/benchmarks. Configuration: see slide 41 and 42.
DCPMM (64B)
Read/Write
DCPMM (64B)
Read
Lowerisbetter

Morecapacity
Gofaster
MINIMIZEDOWNTIME
SAVEMORE
Cost/DBTerabyte
3TBDRAM+6TBIntel®
Optane™DCpersistentmemory =9TBTOTAL
Restarttime
20mins
Restarttime
90seconds
~$62,495USD ~$38,357USD
Faster
restart15,1613x
Cost
savings1639%
Performance results are based on testing as of Jan 30, 2019 and may not reflect all publicly available security updates. No product or component can be absolutely
secure.
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and
MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results
to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that
product when combined with other products. For more information go to www.intel.com/benchmarks. For detailed configs and pricing see slide/page 55.
Columnar store entire reload into DRAM for 1.3TB dataset is 20 mins. Entire system restart before is 32 Minutes and with DC PMEM it is 13.5 Minutes (12 mins for OS +
1.5 mins)
Pricing Guidance as of March 1, 2019. Intel does not guarantee any costs or cost reduction.
You should consult other information and performance tests to assist you in your purchase decision.
CPU: 4 Intel®Xeon® Platinum 8280M Processor
MEMORY: 48 x 128GB DDR4 for 6 TB system
CPU: 4 Intel® Xeon® Platinum 8280L Processor
MEMORY: 24 x 128GB DDR4 + 24 x 256GB Intel Optane DC persistent memory for 9TB system

DDR4DRAMOnly DDR4DRAM with
CPU: Intel® Xeon® Platinum 8276 processor
MEMORY: 768 GB DDR4 DRAM memory
CPU: Intel® Xeon® Platinum 8276 processor
MEMORY: 192 GB DDR4 DRAM memory
+ 1 TB Intel® Optane™ DC persistent memory
Up
to33%More
memory
30%Lowercost
perVM18
Systemmemory
768GB
22vms
~$1,588USD/VM
systemmemory
1TB
30VMs
~$1,108USD/VM
memorymode–WindowsServer®2019/Hyper-V
Up
toSAVEMORE
Up
to36%Morevms
pernode17DOMORE
Performance results are based on testing as of Jan 31, 2019 and may not reflect all publicly available security updates. No product or component can be absolutely secure.
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems,
components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases,
including the performance of that product when combined with other products. For more information go to www.intel.com/benchmarks.
For detailed configs and pricing see slide/page 54.

STORAGE
MEMORY
PERSISTENT
MEMORY
DRAM
HOT TIER
HDD / TAPE
COLD TIER
Intel®3DNandSSDs
INTEL®OPTANE™SSDDCD4800XDUAL-PORT
• PERFORMANCE + RESILIENCY FOR CRITICAL ENTERPRISE IT APPS
• DUAL PORT CONNECTIONS ENABLE 24X7 DATA AVAILABILITY
WITH REDUNDANT, HOT SWAPPABLE DATA PATHS
INTEL®SSDD-5P4326E1.L
• COST-OPTIMIZED, ENABLES GREATER WARM STORAGE
• NEW E1.L FORM FACTOR SCALABLE TO ~1PB (IN 1U)
INTEL.COM/OPTANE

hpcAI/analyticsstorage Infrastructure COMMSdatabase
Hyper-Converged (HCI)
Machine Learning Analytics
ApacheSpark*
RealTime Analytics
SAS*
RDMA/Replication
OracleExadata*
Content Delivery
Network(CDN)
CommsSPcustom
Caching/Persistence
SAPHANA*
MS-SQL*
OracleExadata*
Aerospike*
Redis*
RocksDB*
Scratch&IONodes
HPCFlexMemory
SDS
Ceph*
Memory
VMWareESXi*
MSFTHyper-V*
KVM*
RedisLabs*
Memcached*
VDI
Storage memory
VMware vSAN
Microsoft S2D
Nutanix*
Cisco HyperFlex*
VMWareESXi*
MSFTHyper-V*
KVM*
INTEL.COM/OPTANE
BEST FIT IS SHOWN, BOTH PRODUCTS MAY BE VIABLE

OptimizedLibraries
OptimizedAIFrameworks
Compilers
Profilingtools
Toolkits/
ToolSuites
SOFTWARE.INTEL.COM
ParallelProgrammingFRAMEWORK
Intel® Math Kernel Library for Deep Neural Networks Intel® Machine Learning Scalable Library
Intel® Data Analytics Acceleration Library Intel® Integrated Performance Primitives
TensorFlow*, PyTorch*
Caffe*, PaddlePaddle*
MXNET*, …
Intel® VTune™ Amplifier
Intel® Advisor
Intel® Inspector
Intel® Compilers (LLVM, GCC, Fortran)
nGraph
Intel® Parallel Studio XE Intel® System Studio Intel® Distribution of OpenVINO toolkit
Intel® Distribution for Python* Data Plane Development Kit Persistent Memory Development Kit
Intel® Resource Director Technology (OWCA & Intel® PRM) Storage Performance Development Kit
Threading Building Blocks
Intel® MPI Library
OpenMP*
OWCA: ORCHESTRATION-AWARE WORKLOAD COLLOCATION AGENT
INTEL® PRM: INTEL® PLATFORM RESOURCE MANAGER

UNDER EMBARGO UNTIL 2 APRIL 2019 AT 12:00PM (PACIFIC TIME)
What benefits have you achieved/do you expect to achieve from
Solution Templates from trusted 3rd party?
Source: Reduce Complexity To Maximize Performance, a commissioned study conducted by Forrester Consulting on behalf of Intel, December 2018
https://www.intel.com/content/www/us/en/business/reduce-complexity-maximize-performance.html
TechnicalBenefits BUSINESSBENEFITS
49%
46%
44%
46%
42%
37%
Better System Performance
Improved Security
Faster Deployment
Faster Time To Value
Reduced Operational Costs
Greater ROI
INTEL.COM/SELECTSOLUTIONS

Benchmarked for
specific workloads to
deliver optimal
performance
Workload
optimized
Deploy smoothly
with pre-defined settings
and system-wide tuning
Fast&easy
todeploy
Eliminate guesswork
through tightly specified
HW and SW components
Simplified
evaluation
Simplify and accelerate deployment of workload-optimized infrastructure

Analytics Artificial
Intelligence
Hybrid
Cloud
Network
Transformation
HPC
Microsoft Azure Stack*
Red Hat OpenShift*
Container Platform
Huawei FusionStorage*
Blockchain:
Hyperledger Fabric*
Microsoft SQL Server
Enterprise Data
Warehouse
Linux*
Genomics Analytics
BigDL on Apache Spark*
Universal Customer
Premises Equipment
NFVi: Red Hat*
NFVi: Ubuntu*
NFVi: FusionSphere*
Simulation & Modeling
Professional
Visualization
1H’19 Solution Refresh - Existing Workloads
SAP HANA*
AI InferencingMicrosoft SQL Server
Enterprise Data
Warehouse
Windows Server* VMware vSAN*
Visual Cloud
Delivery Network
HPC AI ConvergedMicrosoft Windows
Server Software Defined*
1H’19 Solutions on NEW Workloads
Microsoft SQL Server*
Business Operations

ISV/OSV DEVELOPMENT PARTNERS OEMs SCALE PARTNERS

BUILDING UPON 20+ YEARS OF INTEL® XEON® PLATFORM LEADERSHIP
INTEL IS LAUNCHING GROUNDBREAKING INNOVATIONS OPTIMIZED FOR DIGITAL TRANSFORMATION
MoveFaster STOREMORE processeverything SOFTWARE&SYSTEM-LEVELOPTIMIZED
PERFORMANCE
IMPROVEMENT
GEN
OVER
GEN
MAINSTREAM PERFORMANCE IMPROVEMENTS
GEN
OVER
GEN
aiperformancewith
intel®dlboost
END-TO-END AI ACCELERATED PROCESSORS
GEN
OVER
GEN
PERFORMANCE
IMPROVEMENT
WORKLOAD SPECIALIZED FOR NFV, CLOUD AND IOT
Memory
capacity
GEN
OVER
GEN
BREAKTHROUGH INNOVATION
Data-centric portfoliolaunch
acceleratingworkloadsfromthemulti-cloudtotheedgeandback
FastestIntel®Xeon®AdoptioninHistory
2 5 12
Performance results are based on testing as of dates shown in configuration and may not reflect all publicly available security updates. Configurations and benchmark details can be found on slide/page 50 and 53. No product or
component can be absolutely secure. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured
using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully
200+PARTNER
DESIGNS
EARLYACCESS
PROGRAM

&

INTEL®XEON®PLATINUM # PROCESSOR2 # # α α
PROCESSORLEVEL
9 PLATINUM
8 PLATINUM
6 GOLD
5 GOLD
4 SILVER
3 BRONZE
PROCESSORGENERATION
2 SECOND GENERATION
1 FIRST GENERATION
PROCESSORSKU
## E.G. 20, 34, … LARGE DDR MEMORY TIER SUPPORT (UP TO 4.5TB)
MEDIUM DDR MEMORY TIER SUPPORT (UP TO 2.0TB)
NETWORKING/NETWORK FUNCTION VIRTUALIZATION
SEARCH
THERMAL
VM DENSITY VALUE
INTEL® SPEED SELECT TECHNOLOGY
L
M
N
S
T
V
Y
PROCESSOROPTIONS
ALL INFORMATION PROVIDED IS SUBJECT TO CHANGE WITHOUT NOTICE.
INTEL MAY MAKE CHANGES TO SPECIFICATIONS AND PRODUCT DESCRIPTIONS AT ANY TIME, WITHOUT NOTICE.
CONTACT YOUR INTEL REPRESENTATIVE TO OBTAIN THE LATEST INTEL PRODUCT SPECIFICATIONS.

NOTICES&DISCLAIMERS
Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies
depending on system configuration.
No product or component can be absolutely secure.
Tests document performance of components on a particular test, in specific systems. Differences in hardware, software, or configuration will affect actual performance.
For more complete information about performance and benchmark results, visit http://www.intel.com/benchmarks .
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and
MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results
to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that
product when combined with other products. For more complete information visit http://www.intel.com/benchmarks .
Intel® Advanced Vector Extensions (Intel® AVX)* provides higher throughput to certain processor operations. Due to varying processor power characteristics, utilizing
AVX instructions may cause a) some parts to operate at less than the rated frequency and b) some parts with Intel® Turbo Boost Technology 2.0 to not achieve any or
maximum turbo frequencies. Performance varies depending on hardware, software, and system configuration and you can learn more at http://www.intel.com/go/turbo.
Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These
optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any
optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors.
Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for
more information regarding the specific instruction sets covered by this notice.
Cost reduction scenarios described are intended as examples of how a given Intel-based product, in the specified circumstances and configurations, may affect future
costs and provide cost savings. Circumstances will vary. Intel does not guarantee any costs or cost reduction.
Intel does not control or audit third-party benchmark data or the web sites referenced in this document. You should visit the referenced web site and confirm whether
referenced data are accurate.
Intel, the Intel logo, and Intel Xeon are trademarks of Intel Corporation in the U.S. and/or other countries.
*Other names and brands may be claimed as property of others.
© 2019 Intel Corporation.

NOTICES&DISCLAIMERS
FTC Optimization Notice
If you make any claim about the performance benefits that can be achieved by using Intel software developer tools (compilers or
libraries), you must use the entire text on the same viewing plane (slide) as the claim.
Optimization Notice: Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel
microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or
effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with
Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User
and Reference Guides for more information regarding the specific instruction sets covered by this notice.
Notice Revision #20110804

FootnotesandConfigurationDetails
Performance results are based on testing as of dates shown in configuration and may not reflect all publicly available security updates. See configuration disclosure for details. No product or component can be absolutely secure. Software and workloads used in
performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to
any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more
complete information visit www.intel.com/benchmarks.
1 - 2x Average Performance Improvement compared with Intel® Xeon® Platinum 8180 processor. Geomean of est SPECrate2017_int_base, est SPECrate2017_fp_base, Stream Triad, Intel Distribution of Linpack, server side Java. Platinum 92xx vs Platinum
8180: 1-node, 2x Intel® Xeon® Platinum 9282 cpu on Walker Pass with 768 GB (24x 32GB 2933) total memory, ucode 0x400000A on RHEL7.6, 3.10.0-957.el7.x86_65, IC19u1, AVX512, HT on all (off Stream, Linpack), Turbo on all (off Stream, Linpack), result: est
int throughput=635, est fp throughput=526, Stream Triad=407, Linpack=6411, server side java=332913, test by Intel on 2/16/2019. vs. 1-node, 2x Intel® Xeon® Platinum 8180 cpu on Wolf Pass with 384 GB (12 X 32GB 2666) total memory, ucode 0x200004D on
RHEL7.6, 3.10.0-957.el7.x86_65, IC19u1, AVX512, HT on all (off Stream, Linpack), Turbo on all (off Stream, Linpack), result: est int throughput=307, est fp throughput=251, Stream Triad=204, Linpack=3238, server side java=165724, test by Intel on 1/29/2019.
2 - Up to 30X AI performance with Intel® DL Boost compared to Intel® Xeon® Platinum 8180 processor (July 2017). Tested by Intel as of 2/26/2019. Platform: Dragon rock 2 socket Intel® Xeon® Platinum 9282(56 cores per socket), HT ON, turbo ON, Total
Memory 768 GB (24 slots/ 32 GB/ 2933 MHz), BIOS:SE5C620.86B.0D.01.0241.112020180249, Centos 7 Kernel 3.10.0-957.5.1.el7.x86_64, Deep Learning Framework: Intel® Optimization for Caffe version: https://github.com/intel/caffe d554cbf1, ICC 2019.2.187,
MKL DNN version: v0.17 (commit hash: 830a10059a018cd2634d94195140cf2d8790a75a), model: https://github.com/intel/caffe/blob/master/models/intel_optimized_models/int8/resnet50_int8_full_conv.prototxt, BS=64, No datalayer DummyData:3x224x224, 56
instance/2 socket, Datatype: INT8 vs Tested by Intel as of July 11th 2017: 2S Intel® Xeon® Platinum 8180 CPU @ 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM.
CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC).Performance measured with: Environment variables: KMP_AFFINITY='granularity=fine, compact‘,
OMP_NUM_THREADS=56, CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time --forward_only” command,
training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from
https://github.com/intel/caffe/tree/master/models/intel_optimized_models (ResNet-50),. Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.
3 – Up to 5.8X better performance than AMD EPYC 7601 compared to Intel® Xeon® Platinum 9282 processor running LINPACK. AMD EPYC 7601: Supermicro AS-2023US-TR4 with 2 AMD EPYC 7601 (2.2GHz, 32 core) processors, SMT OFF, Turbo ON, BIOS
ver 1.1a, 4/26/2018, microcode: 0x8001227, 16x32GB DDR4-2666, 1 SSD, Ubuntu 18.04.1 LTS (4.17.0-041700-generic Retpoline), High Performance Linpack v2.2, compiled with Intel(R) Parallel Studio XE 2018 for Linux, Intel MPI version 18.0.0.128, AMD BLIS
ver 0.4.0, Benchmark Config: Nb=232, N=168960, P=4, Q=4, Score =1095GFs, tested by Intel as of July 31, 2018. vs. 1-node, 2x Intel® Xeon® Platinum 9282 cpu on Walker Pass with 768 GB (24x 32GB 2933) total memory, ucode 0x400000A on RHEL7.6,
3.10.0-957.el7.x86_65, IC19u1, AVX512, HT off, Turbo on, score=6411, test by Intel on 2/16/2019. 1-node, 2x Intel® Xeon® Platinum 8280M cpu on Wolf Pass with 384 GB (12 X 32GB 2933) total memory, ucode 0x400000A on RHEL7.6, 3.10.0-957.el7.x86_65,
IC19u1, AVX512, HT off Linpack, Turbo on, score=3462, test by Intel on 1/30/2019.
4 – Up to 3.50X 5-Year Refresh Performance Improvement VM density compared to Intel® Xeon® E5-2600 v6 processor: 1-node, 2x E5-2697 v2 on Canon Pass with 256 GB (16 slots / 16GB / 1600) total memory, ucode 0x42c on RHEL7.6, 3.10.0-957.el7.x86_65, 1x Intel
400GB SSD OS Drive, 2x P4500 4TB PCIe, 2*82599 dual port Ethernet, Virtualization Benchmark, VM kernel 4.19, HT on, Turbo on, score: VM density=74, test by Intel on 1/15/2019. vs. 1-node, 2x 8280 on Wolf Pass with 768 GB (24 slots / 32GB / 2666) total memory, ucode 0x2000056 on
RHEL7.6, 3.10.0-957.el7.x86_65, 1x Intel 400GB SSD OS Drive, 2x P4500 4TB PCIe, 2*82599 dual port Ethernet, Virtualization Benchmark, VM kernel 4.19, HT on, Turbo on, score: VM density=21, test by Intel on 1/15/2019.
5 –1.33X Average Performance Improvement compared to Intel® Xeon® Gold 5100 Processor: Geomean of est SPECrate2017_int_base, est SPECrate2017_fp_base, Stream Triad, Intel Distribution of Linpack, server side Java. Gold 5218 vs Gold 5118: 1-node,
2x Intel® Xeon® Gold 5218 cpu on Wolf Pass with 384 GB (12 X 32GB 2933 (2666)) total memory, ucode 0x4000013 on RHEL7.6, 3.10.0-957.el7.x86_65, IC18u2, AVX2, HT on all (off Stream, Linpack), Turbo on, result: est int throughput=162, est fp
throughput=172, Stream Triad=185, Linpack=1088, server side java=98333, test by Intel on 12/7/2018. 1-node, 2x Intel® Xeon® Gold 5118 cpu on Wolf Pass with 384 GB (12 X 32GB 2666 (2400)) total memory, ucode 0x200004D on RHEL7.6, 3.10.0-
957.el7.x86_65, IC18u2, AVX2, HT on all (off Stream, Linpack), Turbo on, result: est int throughput=119, est fp throughput=134, Stream Triad=148.6, Linpack=822, server side java=67434, test by Intel on 11/12/2018

6 – Up to 14X AI Performance Improvement with Intel® DL Boost compared to Intel® Xeon® Platinum 8180 Processor (July 2017). Tested by Intel as of 2/20/2019. 2 socket Intel® Xeon® Platinum 8280 Processor, 28 cores HT On Turbo ON Total Memory 384
GB (12 slots/ 32GB/ 2933 MHz), BIOS: SE5C620.86B.0D.01.0271.120720180605 (ucode: 0x200004d), Ubuntu 18.04.1 LTS, kernel 4.15.0-45-generic, SSD 1x sda INTEL SSDSC2BA80 SSD 745.2GB, nvme1n1 INTEL SSDPE2KX040T7 SSD 3.7TB, Deep
Learning Framework: Intel® Optimization for Caffe version: 1.1.3 (commit hash: 7010334f159da247db3fe3a9d96a3116ca06b09a) , ICC version 18.0.1, MKL DNN version: v0.17 (commit hash: 830a10059a018cd2634d94195140cf2d8790a75a, model:
https://github.com/intel/caffe/blob/master/models/intel_optimized_models/int8/resnet50_int8_full_conv.prototxt, BS=64, DummyData, 4 instance/2 socket, Datatype: INT8 vs Tested by Intel as of July 11th 2017: 2S Intel® Xeon® Platinum 8180 CPU @ 2.50GHz (28
cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in
SATA 6Gb/s, 25nm, MLC).Performance measured with: Environment variables: KMP_AFFINITY='granularity=fine, compact‘, OMP_NUM_THREADS=56, CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe:
(http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time --forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other
topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (ResNet-50),. Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries
version 2018.0.20170425. Caffe run with “numactl -l“.
7 – Up to 3.1X Better Performance than AMD EPYC 7601 compared to Intel® Xeon® Platinum 8282 Processor running LINPACK. AMD EPYC 7601: Supermicro AS-2023US-TR4 with 2 AMD EPYC 7601 (2.2GHz, 32 core) processors, SMT OFF, Turbo ON,
BIOS ver 1.1a, 4/26/2018, microcode: 0x8001227, 16x32GB DDR4-2666, 1 SSD, Ubuntu 18.04.1 LTS (4.17.0-041700-generic Retpoline), High Performance Linpack v2.2, compiled with Intel(R) Parallel Studio XE 2018 for Linux, Intel MPI version 18.0.0.128, AMD
BLIS ver 0.4.0, Benchmark Config: Nb=232, N=168960, P=4, Q=4, Score =1095GFs, tested by Intel as of July 31, 2018. vs. 1-node, 2x Intel® Xeon® Platinum 9282 cpu on Walker Pass with 768 GB (24x 32GB 2933) total memory, ucode 0x400000A on RHEL7.6,
3.10.0-957.el7.x86_65, IC19u1, AVX512, HT off, Turbo on, score=6411, test by Intel on 2/16/2019. 1-node, 2x Intel® Xeon® Platinum 8280M cpu on Wolf Pass with 384 GB (12 X 32GB 2933) total memory, ucode 0x400000A on RHEL7.6, 3.10.0-957.el7.x86_65,
IC19u1, AVX512, HT off Linpack, Turbo on, score=3462, test by Intel on 1/30/2019.

8 - 1x inference throughput improvement in July 2017 (baseline): Tested by Intel as of July 11th 2017: Platform: 2S Intel® Xeon® Platinum 8180 CPU @ 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate
driver, 384GB DDR4-2666 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC).Performance measured with: Environment variables:
KMP_AFFINITY='granularity=fine, compact‘, OMP_NUM_THREADS=56, CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference
measured with “caffe time --forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs
from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (ResNet-50),and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but
are functionally equivalent). Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.
9 - 5.7x inference throughput improvement in December 2018 vs baseline: Tested by Intel as of November 11th 2018 :2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total Memory 376.46GB (12slots / 32 GB / 2666
MHz). CentOS Linux-7.3.1611-Core, kernel: 3.10.0-862.3.3.el7.x86_64, SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , Deep Learning Framework Intel® Optimization for caffe version:
551a53d63a6183c233abaa1a19458a25b672ad41 Topology::ResNet_50_v1 BIOS:SE5C620.86B.00.01.0014.070920180847 MKLDNN: 4e333787e0d66a1dca1218e99a891d493dbc8ef1 instances: 2 instances socket:2 (Results on Intel® Xeon® Scalable
Processor were measured running multiple instances of the framework. Methodology described here: https://software.intel.com/en-us/articles/boosting-deep-learning-training-inference-performance-on-xeon-and-xeon-phi) NoDataLayer. Datatype: INT8
Batchsize=64 vs Tested by Intel as of July 11th 2017:2S Intel® Xeon® Platinum 8180 CPU @ 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. CentOS Linux release
7.3.1611 (Core), Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC).Performance measured with: Environment variables: KMP_AFFINITY='granularity=fine, compact‘, OMP_NUM_THREADS=56,
CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time --forward_only” command, training measured with “caffe
time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models
(ResNet-50). Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.
10 – 14x inference throughput improvement vs baseline: Tested by Intel as of 2/20/2019. 2 socket Intel® Xeon® Platinum 8280 Processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2933 MHz), BIOS:
SE5C620.86B.0D.01.0271.120720180605 (ucode: 0x200004d), Ubuntu 18.04.1 LTS, kernel 4.15.0-45-generic, SSD 1x sda INTEL SSDSC2BA80 SSD 745.2GB, nvme1n1 INTEL SSDPE2KX040T7 SSD 3.7TB, Deep Learning Framework: Intel® Optimization for
Caffe version: 1.1.3 (commit hash: 7010334f159da247db3fe3a9d96a3116ca06b09a) , ICC version 18.0.1, MKL DNN version: v0.17 (commit hash: 830a10059a018cd2634d94195140cf2d8790a75a, model:
https://github.com/intel/caffe/blob/master/models/intel_optimized_models/int8/resnet50_int8_full_conv.prototxt, BS=64, DummyData, 4 instance/2 socket, Datatype: INT8 vs Tested by Intel as of July 11th 2017: 2S Intel® Xeon® Platinum 8180 CPU @ 2.50GHz (28
cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in
SATA 6Gb/s, 25nm, MLC).Performance measured with: Environment variables: KMP_AFFINITY='granularity=fine, compact‘, OMP_NUM_THREADS=56, CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe:
(http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time --forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other
topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (ResNet-50),. Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries
version 2018.0.20170425. Caffe run with “numactl -l“.
11 - 30x inference throughput improvement with CascadeLake-AP vs baseline: Tested by Intel as of 2/26/2019. Platform: Dragon rock 2 socket Intel® Xeon® Platinum 9282(56 cores per socket), HT ON, turbo ON, Total Memory 768 GB (24 slots/ 32 GB/
2933 MHz), BIOS:SE5C620.86B.0D.01.0241.112020180249, Centos 7 Kernel 3.10.0-957.5.1.el7.x86_64, Deep Learning Framework: Intel® Optimization for Caffe version: https://github.com/intel/caffe d554cbf1, ICC 2019.2.187, MKL DNN version: v0.17 (commit
hash: 830a10059a018cd2634d94195140cf2d8790a75a), model: https://github.com/intel/caffe/blob/master/models/intel_optimized_models/int8/resnet50_int8_full_conv.prototxt, BS=64, No datalayer DummyData:3x224x224, 56 instance/2 socket, Datatype: INT8
vs Tested by Intel as of July 11th 2017: 2S Intel® Xeon® Platinum 8180 CPU @ 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. CentOS Linux release 7.3.1611 (Core),
Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC).Performance measured with: Environment variables: KMP_AFFINITY='granularity=fine, compact‘, OMP_NUM_THREADS=56, CPU Freq set
with cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time --forward_only” command, training measured with “caffe time”
command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (ResNet-
50),. Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.

12 – Up to 1.25 to 1.58X NVF Workload Performance Improvement comparing Intel® Xeon® Gold 6230N processor to Intel® Xeon® Gold 6130 processor.
VPP IP Security: Tested by Intel on 1/17/2019 1-Node, 2x Intel® Xeon® Gold 6130 Processor on Neon City platform with 12x 16GB DDR4 2666MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 6x Intel XXV710-DA2, Bios:
PLYDCRB1.86B.0155.R08.1806130538, ucode: 0x200004d (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.15.0-42-generic, Benchmark: VPP IPSec w/AESNI (AES-GCM-128) (Max Gbits/s (1420B)), Workload version: VPP v17.10, Compiler: gcc7.3.0, Results: 179. Tested
by Intel on 1/17/2019 1-Node, 2x Intel® Xeon® Gold 6230N Processor on Neon City platform with 12x 16GB DDR4 2999MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 6x Intel XXV710-DA2, Bios: PLYXCRB1.PFT.0569.D08.1901141837, ucode: 0x4000019
(HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.20.0-042000rc6-generic, Benchmark: VPP IPSec w/AESNI (AES-GCM-128) (Max Gbits/s (1420B)), Workload version: VPP v17.10, Compiler: gcc7.3.0, Results: 225
VPP FIB: Tested by Intel on 1/17/2019 1-Node, 2x Intel® Xeon® Gold 6130 Processor on Neon City platform with 12x 16GB DDR4 2666MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 6x Intel XXV710-DA2, Bios: PLYDCRB1.86B.0155.R08.1806130538, ucode:
0x200004d (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.15.0-42-generic, Benchmark: VPP FIB (Max Mpackets/s (64B)), Workload version: VPP v17.10 in ipv4fib configuration, Compiler: gcc7.3.0, Results: 160. Tested by Intel on 1/17/2019 1-Node, 2x Intel® Xeon® Gold
6230N Processor on Neon City platform with 12x 16GB DDR4 2999MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 6x Intel XXV710-DA2, Bios: PLYXCRB1.PFT.0569.D08.1901141837, ucode: 0x4000019 (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel:
4.20.0-042000rc6-generic, Benchmark: VPP FIB (Max Mpackets/s (64B)), Workload version: VPP v17.10 in ipv4fib configuration, Compiler: gcc7.3.0, Results: 212.9
Virtual Firewall: Tested by Intel on 10/26/2018 1-Node, 2x Intel® Xeon® Gold 6130 Processor on Neon City platform with 12x 16GB DDR4 2666MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 4x Intel X710-DA4, Bios: PLYDCRB1.86B.0155.R08.1806130538,
ucode: 0x200004d (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.15.0-42-generic, Benchmark: Virtual Firewall (64B Mpps), Workload version: opnfv 6.2.0, Compiler: gcc7.3.0, Results: 38.9. Tested by Intel on 2/04/2019 1-Node, 2x Intel® Xeon® Gold 6230N Processor on
Neon City platform with 12x 16GB DDR4 2999MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 6x Intel XXV710-DA2, Bios: PLYXCRB1.PFT.0569.D08.1901141837, ucode: 0x4000019 (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.20.0-042000rc6-
generic, Benchmark: Virtual Firewall (64B Mpps), Workload version: opnfv 6.2.0, Compiler: gcc7.3.0, Results: 52.3
Virtual Broadband Network Gateway: Tested by Intel on 11/06/2018 1-Node, 2x Intel® Xeon® Gold 6130 Processor on Neon City platform with 12x 16GB DDR4 2666MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 6x Intel XXV710-DA2, Bios:
PLYDCRB1.86B.0155.R08.1806130538, ucode: 0x200004d (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.15.0-42-generic, Benchmark: Virtual Broadband Network Gateway (88B Mpps), Workload version: DPDK v18.08 ip_pipeline application, Compiler: gcc7.3.0, Results:
56.5. Tested by Intel on 1/2/2019 1-Node, 2x Intel® Xeon® Gold 6230N Processor on Neon City platform with 12x 16GB DDR4 2999MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 6x Intel XXV710-DA2, Bios: PLYXCRB1.PFT.0569.D08.1901141837, ucode:
0x4000019 (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.20.0-042000rc6-generic, Benchmark: Virtual Broadband Network Gateway (88B Mpps), Workload version:DPDK v18.08 ip_pipeline application, Compiler: gcc7.3.0, Results: 78.7
VCMTS: Tested by Intel on 1/22/2019 1-Node, 2x Intel® Xeon® Gold 6130 Processor on Supermicro*-X11DPH-Tq platform with 12x 16GB DDR4 2666MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 4x Intel XXV710-DA2, Bios: American Megatrends Inc.*
version: '2.1', ucode: 0x200004d (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.20.0-042000rc6-generic, Benchmark: Virtual Converged Cable Access Platform (iMIX Gbps), Workload version: vcmts 18.10, Compiler: gcc7.3.0 , Other software: Kubernetes* 1.11, Docker*
18.06, DPDK 18.11, Results: 54.8. Tested by Intel on 1/22/2019 1-Node, 2x Intel® Xeon® Gold 6230N Processor on Neon City platform with 12x 16GB DDR4 2999MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 6x Intel XXV710-DA2, Bios:
PLYXCRB1.PFT.0569.D08.1901141837, ucode: 0x4000019 (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.20.0-042000rc6-generic, Benchmark: Virtual Converged Cable Access Platform (iMIX Gbps), Workload version: vcmts 18.10 , Compiler: gcc7.3.0, Other software:
Kubernetes* 1.11, Docker* 18.06, DPDK 18.11, Results: 83.7
OVS DPDK: Tested by Intel on 1/21/2019 1-Node, 2x Intel® Xeon® Gold 6130 Processor on Neon City platform with 12x 16GB DDR4 2666MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 4x Intel XXV710-DA2, Bios: PLYXCRB1.86B.0568.D10.1901032132,
ucode: 0x200004d (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.15.0-42-generic, Benchmark: Open Virtual Switch (on 4C/4P/8T 64B Mpacket/s), Workload version: OVS 2.10.1, DPDK-17.11.4, Compiler: gcc7.3.0, Other software: QEMU-2.12.1, VPP v18.10, Results: 9.6.
Tested by Intel on 1/18/2019 1-Node, 2x Intel® Xeon® Gold 6230N Processor on Neon City platform with 12x 16GB DDR4 2999MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 6x Intel XXV710-DA2, Bios: PLYXCRB1.86B.0568.D10.1901032132, ucode:
0x4000019 (HT= ON, Turbo= OFF), OS: Ubuntu* 18.04 with kernel: 4.20.0-042000rc6-generic, Benchmark: Open Virtual Switch (on 6P/6C/12T 64B Mpacket/s), Workload version: OVS 2.10.1, DPDK-17.11.4, Compiler: gcc7.3.0, Other software: QEMU-2.12.1, VPP v18.10, Results:
15.2. Tested by Intel on 1/18/2019 1-Node, 2x Intel® Xeon® Gold 6230N Processor with SST-BF enabled on Neon City platform with 12x 16GB DDR4 2999MHz (384GB total memory), Storage: 1x Intel® 240GB SSD, Network: 6x Intel XXV710-DA2, Bios:
PLYXCRB1.86B.0568.D10.1901032132, ucode: 0x4000019 (HT= ON, Turbo= ON (SST-BF)), OS: Ubuntu* 18.04 with kernel: 4.20.0-042000rc6-generic, Benchmark: Open Virtual Switch (on 6P/6C/12T 64B Mpacket/s), Workload version: OVS 2.10.1, DPDK-17.11.4, Compiler:
gcc7.3.0, Other software: QEMU-2.12.1, VPP v18.10, Results: 16.9

13
> 45% latency reduction with Open Source
Redis using 2nd gen Intel® Xeon®
Scalable Processors and Intel®
Ethernet 800 Series with ADQ.
Calculation: (new - old) / old x 100%
Rtt Average Latency across all run for baseline vs ADQ
(382-1249)/1249 * 100% = -69% Reduction
in Rtt Average Latency
14
> 30% throughput improvement with Open Source
Redis using 2nd gen Intel® Xeon®
Scalable Processors and Intel®
Ethernet 800 Series with ADQ.
Calculation: (new - old) / old x 100%
Transaction Request Rate across all run for baseline vs ADQ
(79601-44345)/44345 * 100% = 80% Throughput Improvement
Source: Intel internal testing as of February 2019
Configuration details provided in the following tables:
SUT Client
Test by Intel Intel
Test date 2/11/2019 2/11/2019
Platform Intel® Server Board S2600WFTF Dell* PowerEdge* R720
# Nodes 1 11
# Sockets 2 2
CPU 2nd Generation Intel® Xeon® Scalable processor 8268 @ 2.8GHz Intel® Xeon® processor E5-2697 v2
Cores/socket, Threads/socket 24/48 12 / 24
ucode 0x3000009 0x428
HT On On
Turbo On On
BIOS version SE5C620.86B.01.00.0833.051120182255 2.5.4
System DDR Mem Config: slots / cap / run-speed 8 slots / 128GB / 2400MT/s 16 slots / 128GB / 1600MT/s
System DCPMM Config: slots / cap / run-speed 2 slots / 1024GB -
Total Memory/Node (DDR+DCPMM) 1024GB 128GB
Storage - boot 1x Intel SSD (OS Drive 64GB) 1x Dell (OS Drive 512GB)
Storage - application drives - -
NIC 1x Intel® Ethernet Network Adapter E810-CQDA2 1x Intel® Ethernet X520-DA2
PCH Intel® C620 Series Chipset Intel® C600 Series Chipset
Other HW (Accelerator)
OS CentOS 7.6 CentOS 7.4
Kernel 4.19.18 (Linux.org Stable) 3.10.0-693.21.1.el7
IBRS (0=disable, 1=enable) 1 0
eIBRS (0=disable, 1=enable) 0 0
Retpoline (0=disable, 1=enable) 1 0
IBPB (0=disable, 1=enable) 1 0
PTI (0=disable, 1=enable) 1 1
Mitigation variants (1,2,3,3a,4, L1TF) 1,2,3,L1TF 1,2,3,L1TF
Workload & version Redis 4.0.10 redis-benchmark 4.0.10
Compiler gcc (GCC) 4.8.5 20150623 -
NIC Driver ice 08.15 ixgbe 4.4.0-k-rh7.4

Baseline Config
(DRAM)
AD 2-2-2 Config
System Lightning Ridge (4S) Lightning Ridge (4S)
CPU Intel® Xeon® 8280M Intel® Xeon® 8280L
CPUs per node 4-socket @ 28 core / socket 4-socket @ 28 core / socket
Memory 6TB
48x 128GB DDR4 @ 2666 MT/s
9TB
24x 256GB Intel® Optane™ DC PMEM
24x 128GB DDR4 @ 2666 MT/s
Network 10 GbE Intel X520 NIC 10 GbE Intel X520 NIC
Storage 60x Intel SSD DC S4600 SATA
480GB TB
90x Intel SSD DC S4600 SATA 480GB TB
BIOS WW48’18 WW48’18
OS or VM
version
SUSE 15 SUSE 15
WL Version Intel IT workload Intel IT workload
SAP HANA*
database size
3TB 6TB
Security
mitigations
Variants 1,2,3 enabled Variants 1,2,3 enabled
Date costs
projected
March 1, 2019 March 1, 2019
15 - 13x faster restart time and 39% less cost configs: 16 - 39% less cost pricing details:

17 & 18 - 36% more VMs per node & 30% lower estimated cost per VM configs:
Config1-DDR4 (Similar Cost) Config2-Intel Optane DC persistent
memory (Similar Cost)
Test by Intel Intel
Test date 01/31/2019 01/31/2019
Platform Confidential – Refer to M. Strassmaier if a
need to know exists
Confidential – Refer to M.
Strassmaier if a need to know exists
# Nodes 1 1
# Sockets 2 2
CPU Cascade Lake B0 8272L Cascade Lake B0 8272L
Cores/socket, Threads/socket 26/52 26/52
HT ON ON
Turbo ON ON
BKC version – E.g. ww47 WW42 WW42
Intel Optane DC persistent memory FW
version
5253 5253
System DDR Mem Config: slots / cap / run-
speed
24 slots / 32GB / 2666 12 slots / 16 GB / 2666
System DCPMM Config: slots / cap / run-
speed
8 slots /128GB / 2666
Total Memory/Node (DDR, DCPMM) 768GB, 0 192GB, 1TB
Storage - boot 1x Samsung PM963 M.2 960GB 1x Samsung PM963 M.2 960GB
Storage - application drives 7 x Samsung PM963 M.2 960GB, 4x Intel
SSDs S4600 (1.92TB)
7x Samsung PM963 M.2 960GB, 4x
Intel SSDs S4600 (1.92TB)
NIC 1xIntel X520 SR2 (10Gb) 1x Intel X520 SR2 (10Gb)
PCH LBG QS/PRQ – T – B2 LBG QS/PRQ – T – B2
Other HW (Accelerator)
OS Windows Server 2019 RS5-17763 Windows Server 2019 RS5-17763
Kernel
Workload & version OLTP Cloud Benchmark OLTP Cloud Benchmark
Compiler
Libraries
Other SW (Frameworks, Topologies…)

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