Build Value on Your Critical Workloads
Discover how to take your business into the future with the performance and value you need on your critical workloads.
Improve Cloud Workload Performance
Find out how AWS instances based on Intel® processors can boost cloud workload performance and deliver better value.
Do More with Your Cloud Investment
Deliver the powerful performance per dollar you need on Intel® technology. Critical data-heavy workloads such as database, high performance computing (HPC), and web perform better at a lower total cost of ownership on Intel® architecture-based clouds.1 2 3 4 5
2.84x Higher Performance
Up to 2.84x higher performance/$ on database workloads, including HammerDB PostgreSQL*, and MongoDB*.1
4.15x Higher Performance
Up to 4.15x higher performance/$ on High Performance LINPACK* and LAMMPS*.2
1.74x Better Performance
Better performance/$ on Server-Side Java and 1.74x better performance /$ on WordPress PHP/HHVM.3
2.25x Higher Performance
Up to 2.25x higher performance/$ for memory bandwidth applications.4
AWS Cloud Service Performance Benchmarks
Review cloud service performance benchmarks for Amazon Web Services instances running on Intel.
Discover the Key to Cloud TCO
TCO is critical when choosing a cloud instance for your applications.
Reduce Cost
Drive major cloud infrastructure savings with new advances from Intel and AWS.
Learn how
Plan Your TCO
A guide to creating an enterprise cloud business plan built around an efficient TCO-focused strategy.
Learn how
Compare Cloud TCO
Not all clouds are created equal. Compare affordability and the best fit for your needs.
Learn how
This Is Your Data on Intel
Imagine your infrastructure optimized for the data era. Faster insights from your data. Greater efficiency and ability to scale from your architecture. This is your data on Intel.
Notices and Disclaimers:
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.co.uk/benchmarks.
Performance results are based on testing as of the date indicated in the configuration details and may not reflect all publicly available security updates. See configuration disclosure for details. No product or component can be absolutely secure.
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.
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® 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 https://www.intel.co.uk/content/www/uk/en/architecture-and-technology/turbo-boost/turbo-boost-technology.html.
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.
Product and Performance Information
Performance per dollar testing done on AWS* EC2 R5 and R5a instances (https://aws.amazon.com/ec2/instance-types/), comparing 96 vCPU Intel® Xeon® Scalable processor performance per dollar to AMD EPYC* processor performance per dollar.
Workload: HammerDB* PostgreSQL*
Results: AMD EPYC performance per dollar = baseline of 1; Intel® Xeon® Scalable processor performance per dollar = 1.85X (higher is better)
Database: HammerDB – PostgreSQL (higher is better):
AWS R5.24xlarge (Intel) Instance, HammerDB 3.0 PostgreSQL 10.2, Memory: 768GB, Hypervisor: KVM; Storage Type: EBS io1, Disk Volume 200GB, Total Storage 200GB, Docker version: 18.06.1-ce, RedHat* Enterprise Linux 7.6, 3.10.0-957.el7.x86_64, 6400MB shared_buffer, 256 warehouses, 96 users. Score “NOPM” 439931, measured by Intel on 12/11/18-12/14/18.
AWS R5a.24xlarge (AMD) Instance, HammerDB 3.0 PostgreSQL 10.2, Memory: 768GB, Hypervisor: KVM; Storage Type: EBS io1, Disk Volume 200GB, Total Storage 200GB, Docker version: 18.06.1-ce, RedHat* Enterprise Linux 7.6, 3.10.0-957.el7.x86_64, 6400MB shared_buffer, 256 warehouses, 96 users. Score “NOPM” 212903, measured by Intel on 12/20/18.
Workload: MongoDB*
Results: AMD EPYC performance per dollar = baseline of 1; Intel® Xeon® Scalable processor performance per dollar = 2.84X (higher is better)
Database: MongoDB (higher is better):
AWS R5.24xlarge (Intel) Instance, MongoDB v4.0, journal disabled, sync to filesystem disabled, wiredTigeCache=27GB, maxPoolSize = 256; 7 MongoDB instances, 14 client VMs, 1 YCSB client per VM, 96 threads per YCSB client, RedHat* Enterprise Linux 7.5, Kernel 3.10.0-862.el7.x86_64, Score 1229288 ops/sec, measured by Intel on 12/10/18.
AWS R5a.24xlarge (AMD) Instance, MongoDB v4.0, journal disabled, sync to filesystem disabled, wiredTigeCache=27GB, maxPoolSize = 256; 7 MongoDB instances, 14 client VMs, 1 YCSB client per VM, 96 threads per YCSB client, RedHat* Enterprise Linux 7.5, Kernel 3.10.0-862.el7.x86_64, Score 388596 ops/sec, measured by Intel on 12/10/18.
For more details visit www.intel.co.uk/benchmarks.
Results calculated by Intel P2CA using AWS pricing ($/hour, standard 1-year term, no up-front) as of 12th January, 2019.
Performance per dollar testing done on AWS* EC2 M5 and M5a instances (https://aws.amazon.com/ec2/instance-types/), comparing 96 vCPU Intel® Xeon® Scalable processor performance per dollar to AMD EPYC* processor performance per dollar.
Workload: LAMMPS*
Results: AMD EPYC performance per dollar = baseline of 1; Intel® Xeon® Scalable processor performance per dollar = 4.15X (higher is better)
HPC Materials Science – LAMMPS (higher is better):
AWS M5.24xlarge (Intel) Instance, LAMMPS version: 2018-08-22 (Code: https://lammps.sandia.gov/download.html), Workload: Water – 512K Particles, Intel ICC 18.0.3.20180410, Intel® MPI Library for Linux* OS, Version 2018 Update 3 Build 20180411, 48 MPI Ranks, RedHat* Enterprise Linux 7.5, Kernel 3.10.0-862.el7.x86_64, OMP_NUM_THREADS=2, Score 137.5 timesteps/sec, measured by Intel on 10/31/18.
AWS M5a.24xlarge (AMD) Instance, LAMMPS version: 2018-08-22 (Code: https://lammps.sandia.gov/download.html), Workload: Water – 512K Particles, Intel ICC 18.0.3.20180410, Intel® MPI Library for Linux* OS, Version 2018 Update 3 Build 20180411, 48 MPI Ranks, RedHat* Enterprise Linux 7.5, Kernel 3.10.0-862.el7.x86_64, OMP_NUM_THREADS=2, Score 55.8 timesteps/sec, measured by Intel on 11/7/18.
Changes for AMD to support AVX2 (AMD only supports AVX2, so these changes were necessary):
sed -i 's/-xHost/-xCORE-AVX2/g' Makefile.intel_cpu_intelmpi
sed -i 's/-qopt-zmm-usage=high/-xCORE-AVX2/g' Makefile.intel_cpu_intelmpi
Workload: High-performance Linpack*
Results: AMD EPYC performance per dollar = baseline of 1; Intel® Xeon® Scalable processor performance per dollar = 4.15X (higher is better)
HPC Linpack (higher is better):
AWS M5.24xlarge (Intel) Instance, HP Linpack Version 2.2 (https://software.intel.com/en-us/articles/intel-mkl-benchmarks-suite Directory: benchmarks_2018.3.222/linux/mkl/benchmarks/mp_linpack/bin_intel/intel64), Intel ICC 18.0.3.20180410 with AVX512, Intel® MPI Library for Linux* OS, Version 2018 Update 3 Build 20180411, RedHat* Enterprise Linux 7.5, Kernel 3.10.0-862.el7.x86_64, OMP_NUM_THREADS=24, 2 MPI processes, Score 3152 GB/s, measured by Intel on 10/31/18.
AWS M5a.24xlarge (AMD) Instance, HP Linpack Version 2.2, (HPL Source: http://www.netlib.org/benchmark/hpl/hpl-2.2.tar.gz; Version 2.2; icc (ICC) 18.0.2 20180210 used to compile and link to BLIS library version 0.4.0; https://github.com/flame/blis; Addt’l Compiler flags: -O3 -funroll-loops -W -Wall –qopenmp; make arch=zen OMP_NUM_THREADS=8; 6 MPI processes.), Intel ICC 18.0.3.20180410 with AVX2, Intel® MPI Library for Linux* OS, Version 2018 Update 3 Build 20180411, RedHat* Enterprise Linux 7.5, Kernel 3.10.0-862.el7.x86_64, OMP_NUM_THREADS=8, 6 MPI processes, Score 677.7 GB/s, measured by Intel on 11/7/18.
Results calculated by Intel P2CA using AWS pricing ($/hour, standard 1-year term, no up-front) as of 12th January, 2019.
Performance per dollar testing done on AWS* EC2 M5 and M5a instances (https://aws.amazon.com/ec2/instance-types/), comparing 96 vCPU Intel® Xeon® Scalable processor performance per dollar to AMD EPYC* processor performance per dollar.
Workload: Server Side Java* 1 JVM
Results: AMD EPYC performance per dollar = baseline of 1; Intel® Xeon® Scalable processor performance per dollar = 1.74X (higher is better)
Server Side Java (higher is better):
AWS M5.24xlarge (Intel) Instance, Java Server Benchmark No NUMA binding, 2JVM, OpenJDK 10.0.1, RedHat* Enterprise Linux 7.5, Kernel 3.10.0-862.el7.x86_64, Score 101767 Transactions/sec, measured by Intel on 11/16/18.
AWS M5a.24xlarge (AMD) Instance, Java Server Benchmark No NUMA binding, 2JVM, OpenJDK 10.0.1, RedHat* Enterprise Linux 7.5, Kernel 3.10.0-862.el7.x86_64, Score 52068 Transactions/sec, measured by Intel on 11/16/18.
Workload: Wordpress* PHP/HHVM*
Results: AMD EPYC performance per dollar = baseline of 1; Intel® Xeon® Scalable processor performance per dollar = 1.75X (higher is better)
Web Front End Wordpress (higher is better):
AWS M5.24xlarge (Intel) Instance, oss-performance/wordpress Ver 4.2.0; Ver 10.2.19-MariaDB-1:10.2.19+maria~bionic; Workload Version': u'4.2.0; Client Threads: 200; PHP 7.2.12-1; perfkitbenchmarker_version="v1.12.0-944-g82392cc; Ubuntu 18.04, Kernel Linux 4.15.0-1025-aws, Score 3626.11 TPS, measured by Intel on 11/16/18.
AWS M5a.24xlarge (AMD) Instance, oss-performance/wordpress Ver 4.2.0; Ver 10.2.19-MariaDB-1:10.2.19+maria~bionic; Workload Version': u'4.2.0; Client Threads: 200; PHP 7.2.12-1; perfkitbenchmarker_version="v1.12.0-944-g82392cc; Ubuntu 18.04, Kernel Linux 4.15.0-1025-aws, Score 1838.48 TPS, measured by Intel on 11/16/18.
For more details visit www.intel.co.uk/benchmarks.
AWS M5.4xlarge (Intel) Instance, McCalpin Stream (OMP version), (Source: https://www.cs.virginia.edu/stream/FTP/Code/stream.c); Intel ICC 18.0.3 20180410 with AVX512, -qopt-zmm-usage=high, -DSTREAM_ARRAY_SIZE=134217728 -DNTIMES=100 -DOFFSET=0 –qopenmp, -qoptstreaming-stores always -o $OUT stream.c, Red Hat* Enterprise Linux 7.5, Kernel 3.10.0-862.el7.x86_64, OMP_NUM_THREADS: 8, KMP_AFFINITY: proclist=[0-7:1], granularity=thread, explicit, Score 81216.7 MB/s, measured by Intel on 12/6/18.
AWS M5a.4xlarge (AMD) Instance, McCalpin Stream (OMP version), (Source: https://www.cs.virginia.edu/stream/FTP/Code/stream.c); Intel ICC 18.0.3 20180410 with AVX2, -DSTREAM_ARRAY_SIZE=134217728, -DNTIMES=100 -DOFFSET=0 -qopenmp -qopt-streaming-stores always -o $OUT stream.c, Red Hat* Enterprise Linux 7.5, Kernel 3.10.0-862.el7.x86_64, OMP_NUM_THREADS: 8, KMP_AFFINITY : proclist=[0-7:1], granularity=thread,explicit, Score 32154.4 MB/s, measured by Intel on 12/6/18.
OpenFOAM Disclaimer: This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software via www.openfoam.com, and owner of the OpenFOAM* and OpenCFD* trademark.
AWS pricing as of 12th January 2019 Standard 1-Year term Reserved Instance Pricing (https://aws.amazon.com/ec2/pricing/reserved-instances/pricing/) On Demand Linux/Unix Usage Pricing per hour (https://aws.amazon.com/ec2/pricing/on-demand/).
Up to 30x Inference Throughput Improvement on Intel® Xeon® Platinum 9282 processor with Intel® Deep Learning Boost (Intel® DL Boost): Tested by Intel as of 2/26/2019. Platform: Dragon rock 2 socket Intel® Xeon® Platinum 9282 processor (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 syntheticData:3x224x224, 56 instance/2 socket, Datatype: INT8 vs Tested by Intel as of July 11th 2017: 2S Intel® Xeon® Platinum 8180 processor 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 Data Center 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 CPU Power 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, synthetic 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® Math Kernel Library (Intel® MKL) small libraries version 2018.0.20170425. Caffe run with "numactl -l".