Supercomputing Innovations and Breakthroughs
HPC and AI at Exascale
Intel is engineering the first US exascale supercomputer, built on future Intel® Xeon® Scalable processors, Intel® Xeon® compute architecture, Intel® Optane™ DC persistent memory, and more.
DAOS Revolutionizes High Performance Storage
Learn how we're revolutionizing high performance storage with DAOS, our open-source software stack, and Intel® Optane™ technology.
Manufacturing, Life Sciences, and Oil & Gas
HPC is rapidly transforming as Intel® Xeon® Scalable processors power the next wave of analytics, simulation & modeling, and AI.
KISTI Changes Supercomputing in Korea
KISTI with its NURION supercomputer is growing to broaden the leadership of HPC R&D in Korea.
Supporting Demanding Computational Science
The Marconi supercomputer is a critical step to Cineca achieving their exascale computing objectives.
Osaka Supercomputer Supports Research
Osaka University's OCTOPUS Supercomputer has boosted scientific capacity and supports a wide range of projects.
Introducing the 2nd Generation Intel® Xeon® Scalable Processors
Discover the flexibility of one platform to handle your AI, simulation and modeling, analytics and other general HPC workloads.
Performance for HPC Platforms
Our latest platform with the 2nd Gen Intel® Xeon® Scalable processor reduces the need for dedicated systems running specialized hardware and software for unique workloads.
Leadership Compute Performance
Learn how the new Intel® Xeon® Platinum 9200 processors and Intel® Server System S9200WK are designed to optimize performance for HPC and AI workloads.
HLRN Brings Advanced Performance to HPC
HLRN selected the new Intel® Xeon® Platinum 9200 processors to meet their increasingly diverse needs for HPC workloads.
TACC Offers New HPC Capabilities
The Texas Advanced Computing Center (TACC) is using 2nd Generation Intel® Xeon® Scalable processors to solve simulation-based and data-driven science problems.
The Convergence of AI and HPC
AI on the Intel®-based HPC Infrastructure You Know
Artificial intelligence (AI) has the potential to change our lives. It’s powerful and far reaching yet does not require separate highly specialized hardware and software systems to run. Your familiar Intel®-based HPC infrastructure handles AI along with your big-data analytical workloads and more: For more insight, discovery, and competitive advantage.
Watch “AI and HPC Workload Convergence” ›
Running AI and Analytics in a Single Cluster Infrastructure
This solution brief introduces the challenges and opportunities around running AI and analytics workloads on existing HPC clusters.
AI on HPC Infrastructure in Three Steps
There are a number of steps to successfully integrating AI workloads into your existing HPC environment — our visual guide will help you get started.
Supporting HPC, AI, Analytics on a Common Platform
Learn about capabilities for running TensorFlow* and Apache Spark* on existing HPC systems using standard batch schedulers.
Intel® Select Solutions for HPC
Intel® Select Solutions are pre-configured HPC stacks designed and optimized for specific data-center workloads — like professional visualization, simulation and modeling, and genomic analytics. Available through Intel’s trusted OEM partners, Intel® Select Solutions will accelerate your HPC plans.
Intel® HPC Platform Specification and Community
Intel® HPC Platform Specification
See the recommended combination of compute, fabric, memory, storage, and software elements that comprise the fundamental requirements for Intel® Select Solutions.
Intel® HPC Application Catalog
View a centralized list of applications verified compatible with Intel® HPC Platform Specification and Intel® Select Solutions for HPC.
Intel® Cluster Checker Tool
Verify that cluster components work together seamlessly for improved uptime, productivity, and lower total cost of ownership (TCO).
Intel HPC Products and Technology
Intel® Xeon® Scalable Processors
Drive actionable insight, count on hardware-based security, and deploy dynamic service delivery with Intel® Xeon® Scalable processors.
Intel® Deep Learning Boost (Intel® DL Boost)
Intel® Xeon® Scalable processors take embedded AI performance to the next level with Intel® Deep Learning Boost (Intel® DL Boost).
Intel® Optane™ Technology
We’re enabling solutions that unleash CPU utilization, reduce bottlenecks, and deliver unprecedented insights from large datasets.
Intel® Omni-Path Architecture (Intel® OPA)
Intel® Omni-Path Architecture (Intel® OPA) lowers system TCO while providing reliability, high performance, and extreme scalability.
Data Center Storage Solutions
With the explosion of data, modernizing storage is critical to IT transformation. Advances in technology allow for more efficient storage, access, and transfer of data.
Intel® FPGAs
From the IoT to the data center, Intel® FPGA solutions deliver the speed and capacity of full systems-on-chip.
Intel® Ethernet Network Adapter
Intel® Ethernet Network Adapters, Controllers, and Accessories deliver services efficiently and cost-effectively in the data center.
HPC Software and Tools
Modernize your code for today's and tomorrow’s hardware using advanced tools that help build, debug, and tune your applications.
Stay Connected
Stay connected to technologies, trends, and ideas that are shaping the future of the workplace with the Intel IT Center.
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Product and Performance Information
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 11, 2017: 2S Intel® Xeon® Platinum 8180 processor @ 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 (800 GB, 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, 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”.
4X Linpack performance with 2nd Gen Intel® Xeon® Platinum 9242 processor vs AMD* EPYC* 7601 at scale (4-node, 8-node).
Intel® Xeon® 9242 Processor: Intel Reference Platform with 2S Intel® Xeon® 9242 processors (2.2GHz, 48C), 16x16GB DDR4-2933, 1 SSD, Cluster File System: 2.12.0-1 (server) 2.11.0-14.1 (client), BIOS: PLYXCRB1.86B.0572.D02.1901180818, Microcode: 0x4000017, CentOS* 7.6, Kernel: 3.10.0-957.5.1.el7.x86_64, OFED stack: OFED OPA 10.8 on RH7.5 with Lustre v2.10.4, HBA: 100Gbps Intel® Omni-Path Architecture (Intel® OPA) 1 port PCIe* x16, Switch: (Intel® OPA) Edge Switch 100 Series 48 Port, HPL 2.1, Intel Compiler 2019u1, Intel® Math Kernel Library (Intel® MKL) 2019, Intel MPI 2019u1, HT=ON, Turbo=OFF, 2 threads per core, 4-node=20,408.00, 8-node=39921 GF/s, higher is better, test by Intel on 3/3/2019.
AMD EPYC 7601: Supermicro AS -1023US-TR4, 2S AMD EPYC 7601 (2.2GHz, 32C), 16x16GB DDR4-2666, 1 SSD, BIOS ver: 1.1b (08/20/2018), Microcode ver: 0x8001227, Oracle* Linux Server release 7.5 (3.10.0-862.14.4.el7.crt1.x86_64), Cluster File System: Panasas (124 TB storage) Firmware version 5.5.0.b-1067797.15 EDR based IEEL Lustre, 100Gbps Mellanox EDR MT27700, 36 Port Mellanox EDR IB Switch, OFED MLNX mlnx-4.3-3.0.2.0, HPL 2.2, Intel Compiler 2018u3, AMD BLIS v0.4.0, Intel MPI 2018u3, SMT=ON,Turbo=ON, 2 threads per core, 4Node=4739.96, 8Node=9406.07 GF/s, higher is better, test by Intel on 9/23/2018.
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.co.uk/benchmarks.
Performance results are based on testing as of the dates set forth 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® technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. Check with your system manufacturer or retailer or learn more at intel.co.uk.