Artificial Intelligence with 2nd Gen Intel® Xeon® Scalable Processor

The 2nd Gen Intel® Xeon® Scalable processor provides scalable performance for the widest variety of datacenter workloads – including deep learning. The new 2nd Gen Intel® Xeon® Scalable processor platform offers built-in Return on Investment (ROI), potent performance and production-ready support for AI deployments.

In our smart and connected world, machines are increasingly learning to sense, reason, act, and adapt in the real world. Artificial Intelligence (AI) is the next big wave of computing, and Intel uniquely has the experience to fuel the AI computing era. AI will let us accelerate solutions to large-scale problems that would otherwise take months, years, or decades to resolve.

AI will unleash new scientific discoveries, automate undesirable tasks and extend our human senses and capabilities. Today, machine learning (ML) and deep learning (DL) are two underlying approaches to AI, as are reasoning-based systems.

Deep learning is the most rapidly emerging branch of machine learning, in many cases supplanting classic ML, relying on massive labeled data sets to iteratively “train” many-layered neural networks inspired by the human brain. Trained neural networks are used to “infer” the meaning of new data, with increased speed and accuracy for processes like image search, speech recognition, natural language processing, and other complex tasks.

The 2nd Generation Intel® Xeon® Scalable processors take AI performance to the next level with Intel® Deep Learning Boost (Intel® DL Boost), a new set of embedded processor technologies designed to accelerate AI deep learning use cases such as image recognition, object detection, speech recognition, language translation, and others. It extends Intel® Advanced Vector Extensions 512 (Intel® AVX-512) with a new Vector Neural Network Instruction (VNNI) that significantly increases deep learning inference performance over previous generations. With 2nd Gen Intel® Xeon® Platinum 8280 processors and Intel® Deep Learning Boost (Intel® DL Boost), we project that image recognition with Intel optimized Caffe ResNet-50 can perform up to 14x1 faster than on prior generation Intel® Xeon® Scalable processors (at launch, July 2017).

Here we present some AI workloads showing advanced inference performance with the 2nd Gen Intel® Xeon® Scalable processors family.

All performance measurements are accurate as of April 2, 2019.1 2 3 4

ResNet-50 performance with Intel® Optimization for Caffe*

Designed for high performance computing, advanced artificial intelligence and analytics, and high density infrastructures Intel® Xeon® Platinum 9200 processors deliver breakthrough levels of performance. Using Intel® Deep Learning Boost (Intel® DL Boost) combined with Intel optimized Caffe, new breakthrough levels of performance can be achieved. Here we show the throughput on an image classification topology – ResNet-50 on the 2nd Generation Intel® Xeon Scalable processor.

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Configuration Details

Max Inference throughput at <7ms

Intel® Xeon® Platinum 8280 processor: Tested by Intel as of 3/04/2019. 2S Intel® Xeon® Platinum 8280(28 cores per socket) processor, HT ON, turbo ON, Total Memory 384 GB (12 slots/ 32 GB/ 2933 MHz), BIOS: SE5C620.86B.0D.01.0348.011820191451, Centos 7 Kernel 3.10.0-957.5.1.el7.x86_64, Intel® Deep Learning Framework: Intel® Optimization for Caffe* version: https://github.com/intel/caffe Commit id: 362a3b3, ICC 2019.2.187 for build, 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=10, synthetic Data:3x224x224, 2 instance/2 socket, Datatype: INT8; latency: 6.16 ms.

Intel® Xeon® Platinum 9242 processor: Tested by Intel as of 3/04/2019 2S Intel® Xeon® Platinum 9242(48 cores per socket) processor, HT ON, turbo ON, Total Memory 768 GB (24 slots/ 32 GB/ 2933 MHz), BIOS: SE5C620.86B.0D.01.0403.022020190327, Centos 7 Kernel 3.10.0-957.5.1.el7.x86_64, Intel® Deep Learning Framework: Intel® Optimization for Caffe* version: https://github.com/intel/caffe Commit id: 362a3b3, ICC 2019.2.187 for build, 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= 8, synthetic Data:3x224x224, 4 instance/2 socket, Datatype: INT8; latency: 6.90 ms.

Intel® Xeon® Platinum 9282 processor: Tested by Intel as of 3/04/2019. DL Inference: Platform: Dragon rock 2S Intel® Xeon® Platinum 9282(56 cores per socket) processor, 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, Intel® Deep Learning Framework: Intel® Optimization for Caffe* version: https://github.com/intel/caffe Commit id: 362a3b3, ICC 2019.2.187 for build, 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=12, synthetic Data:3x224x224, 4 instance/2 socket, Datatype: INT8; latency: 6.91 ms.

Max Inference throughput

Intel® Xeon® Platinum 8280 processor: Tested by Intel as of 3/04/2019. 2S Intel® Xeon® Platinum 8280(28 cores per socket) processor, HT ON, turbo ON, Total Memory 384 GB (12 slots/ 32 GB/ 2933 MHz), BIOS: SE5C620.86B.0D.01.0348.011820191451, Centos 7 Kernel 3.10.0-957.5.1.el7.x86_64, Intel® Deep Learning Framework: Intel® Optimization for Caffe* version: https://github.com/intel/caffe Commit id: 362a3b3, ICC 2019.2.187 for build, 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=10, syntheticData:3x224x224, 14 instance/2 socket, Datatype: INT8.

Intel® Xeon® Platinum 9242 processor: Tested by Intel as of 3/04/2019 2S Intel® Xeon® Platinum 9242(48 cores per socket) processor, HT ON, turbo ON, Total Memory 768 GB (24 slots/ 32 GB/ 2933 MHz), BIOS: SE5C620.86B.0D.01.0403.022020190327, Centos 7 Kernel 3.10.0-957.5.1.el7.x86_64, Intel® Deep Learning Framework: Intel® Optimization for Caffe* version: https://github.com/intel/caffe Commit id: 362a3b3, ICC 2019.2.187 for build, 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=8, synthetic Data:3x224x224, 24 instance/2 socket, Datatype: INT8.

Intel® Xeon® Platinum 9282 processor: Tested by Intel as of 3/04/2019. DL Inference: Platform: Dragon rock 2S Intel® Xeon® Platinum 9282(56 cores per socket) processor, 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, Intel® Deep Learning Framework: Intel® Optimization for Caffe*
version: https://github.com/intel/caffe Commit id: 362a3b3, ICC 2019.2.187 for build, 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=8, synthetic Data:3x224x224, 28 instance/2 socket, Datatype: INT8.

BKMs for running multi-stream configurations on Xeon: https://www.intel.ai/wp-content/uploads/sites/69/TensorFlow_Best_Practices_Intel_Xeon_AI-HPC_v1.1_Q119.pdf

ResNet-50 Inference Throughput Performance

Inference () generally happens instantaneously at the edge or in the data center, such as when a new photo is uploaded for inspection. For inference throughput TCO is really critical. Inference output can be fed into a number of different usages including – a dashboard for visualization or a decision tree for automatic decision making. Here we show inference throughput on an image database using multiple popular deep learning frameworks such as Caffe, TensorFlow, Pytorch, and MxNet with the ResNet-50 topology.

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Configuration Details

3.0x performance boost with MxNet on ResNet-50: Tested by Intel as of 3/1/2019. 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:0x4000013),CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: MxNet https://github.com/apache/incubator-mxnet/ -b master da5242b732de39ad47d8ecee582f261ba5935fa9, Compiler: gcc 4.8.5,MKL DNN version: v0.17, ResNet50: https://github.com/apache/incubator-MXNet/blob/master/python/MXNet/gluon/model_zoo/vision/resnet.py, BS=64, synthetic data, 2 instance/2 socket, 0.12% accuracy loss ,Datatype: INT8 vs Tested by Intel as of 3/1/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0286.121520181757, CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: MxNet https://github.com/apache/incubator-mxnet/ -b master da5242b732de39ad47d8ecee582f261ba5935fa9, Compiler: gcc 4.8.5,MKL DNN version: v0.17, ResNet50: https://github.com/apache/incubator-MXNet/blob/master/python/MXNet/gluon/model_zoo/vision/resnet.py, BS=64, synthetic data, 2 instance/2 socket, Datatype: FP32

3.7x performance boost with Pytorch ResNet-50: Tested by Intel as of 2/25/2019. 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: 0x4000013), Ubuntu 18.04.1 LTS, kernel 4.15.0-45-generic, SSD 1x sda INTEL SSDSC2BA80 SSD 745.2GB, 3X INTEL SSDPE2KX040T7 SSD 3.7TB , Intel® Deep Learning Framework: Pytorch with ONNX/Caffe2 backend: https://github.com/pytorch/pytorch.git (commit: 4ac91b2d64eeea5ca21083831db5950dc08441d6)and Pull Request link: https://github.com/pytorch/pytorch/pull/17464 (submitted for upstreaming), gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, MKL DNN version: v0.17.3 (commit hash: 0c3cb94999919d33e4875177fdef662bd9413dd4), ResNet-50: https://github.com/intel/optimized-models/tree/master/pytorch, BS=512, synthetic data, 2 instance/2 socket, 0.6% accuracy loss; Datatype: INT8 vs Tested by Intel as of 2/25/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 192 GB (12 slots/ 16GB/ 2666 MHz), BIOS: SE5C620.86B.00.01.0015.110720180833 (ucode: 0x200004d), CentOS 7.5, 3.10.0-693.el7.x86_64, Intel® SSD DC S4500 SERIES SSDSC2KB480G7 2.5’’ 6Gb/s SATA SSD 480G, Intel® Deep Learning Framework: https://github.com/pytorch/pytorch.git (commit:4ac91b2d64eeea5ca21083831db5950dc08441d6)and Pull Request link: https://github.com/pytorch/pytorch/pull/17464 (submitted for upstreaming), gcc (Red Hat 5.3.1-6) 5.3.1 20160406, MKL DNN version: v0.17.3 (commit hash: 0c3cb94999919d33e4875177fdef662bd9413dd4), ResNet-50: https://github.com/intel/optimized-models/tree/master/pytorch, BS=512, synthetic data, 2 instance/2 socket, Datatype: FP32

3.9x performance boost with TensorFlow ResNet-50:Tested by Intel as of 3/1/2019. 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:0x4000013),CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: https://hub.docker.com/r/intelaipg/intel-optimized-tensorflow:PR25765-devel-mkl (https://github.com/tensorflow/tensorflow.git commit: 6f2eaa3b99c241a9c09c345e1029513bc4cd470a + Pull Request PR 25765, PR submitted for upstreaming) Compiler: gcc 6.3.0,MKL DNN version: v0.17, ResNet50: https://github.com/IntelAI/models/tree/master/models/image_recognition/tensorflow/resnet50, (commit: 87261e70a902513f934413f009364c4f2eed6642) BS=128, synthetic data, 2 instance/2 socket, 0.45% accuracy loss Datatype: INT8 vs Tested by Intel as of 3/1/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0286.121520181757, CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: https://hub.docker.com/r/intelaipg/intel-optimized-tensorflow:PR25765-devel-mkl 6f2eaa3b99c241a9c09c345e1029513bc4cd470a + PR25765, PR submitted for upstreaming) Compiler: gcc 6.3.0,MKL DNN version: v0.17, ResNet50: https://github.com/IntelAI/models/tree/master/models/image_recognition/tensorflow/resnet50 , (commit: 87261e70a902513f934413f009364c4f2eed6642) BS=128, synthetic data, 2 instance/2 socket, Datatype: FP32

4.0x performance boost with Intel® Optimization for Caffe* ResNet-50: 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: 0x4000013), Ubuntu 18.04.1 LTS, kernel 4.15.0-45-generic, SSD 1x sda INTEL SSDSC2BA80 SSD 745.2GB, 3X INTEL SSDPE2KX040T7 SSD 3.7TB , Intel® 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, syntheticData, 2 instance/2 socket, Datatype: INT8 vs Tested by Intel as of 2/21/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 192 GB (12 slots/ 16GB/ 2666 MHz), BIOS: SE5C620.86B.00.01.0015.110720180833 (ucode: 0x200004d), CentOS 7.5, 3.10.0-693.el7.x86_64, Intel® SSD DC S4500 SERIES SSDSC2KB480G7 2.5’’ 6Gb/s SATA SSD 480G, , Intel® 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/benchmark/resnet_50/deploy.prototxt, BS=64, synthetic Data, 2 instance/2 socket, Datatype: FP32

Inference Throughput Performance

The 2nd Gen Intel® Xeon® Scalable processors are built specifically to run high-performance AI and IoT workloads on the same hardware as other existing workloads. Intel® Deep Learning Boost (Intel® DL Boost) can benefit many inference applications ranging from recommendation systems, Object detection and image recognition and classification. Here we show inference throughput for image classification, object detection and a recommendation system. Multiple frameworks are used including TensorFlow*, Caffe2 and MxNet* and multiple topologies such as ResNet-101, Inception v3, RETINANET*, SSD-VGG16 and Wide and Deep.

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Configuration Details

4.0x performance boost with TensorFlow ResNet-101: Tested by Intel as of 3/1/2019. 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:0x4000013),CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: https://hub.docker.com/r/intelaipg/intel-optimized-tensorflow:PR25765-devel-mkl (https://github.com/tensorflow/tensorflow.git commit: 6f2eaa3b99c241a9c09c345e1029513bc4cd470a + Pull Request PR 25765, PR submitted for upstreaming), Compiler: gcc 6.3.0,MKL DNN version: v0.17, ResNet 101 : https://github.com/IntelAI/models/tree/master/models/image_recognition/tensorflow/resnet101 commit: 87261e70a902513f934413f009364c4f2eed6642 , BS=128, synthetic data, 2 instance/2 socket, 0.58% accuracy loss Datatype: INT8 vs Tested by Intel as of 3/1/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0286.121520181757, CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: https://hub.docker.com/r/intelaipg/intel-optimized-tensorflow:PR25765-devel-mkl (https://github.com/tensorflow/tensorflow.git commit: 6f2eaa3b99c241a9c09c345e1029513bc4cd470a + Pull Request PR 25765, PR submitted for upstreaming) Compiler: gcc 6.3.0,MKL DNN version: v0.17, ResNet 101 : https://github.com/IntelAI/models/tree/master/models/image_recognition/tensorflow/resnet101 commit: 87261e70a902513f934413f009364c4f2eed6642 , BS=128, synthetic data, 2 instance/2 socket, Datatype: FP32

3.8x performance boost with MxNet ResNet 101: Tested by Intel as of 3/1/2019. 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:0x4000013),CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: MxNet https://github.com/apache/incubator-mxnet.git commit: da5242b732de39ad47d8ecee582f261ba5935fa9 , Compiler: gcc 4.8.5,MKL DNN version: v0.17, ResNet 101: https://github.com/apache/incubator-MXNet/blob/master/python/MXNet/gluon/model_zoo/vision/resnet.py ,BS= 64, Synthetic data, 2 instance/2 socket, 0.56% accuracy loss, Datatype: INT8 vs Tested by Intel as of 3/1/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0286.121520181757, CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: MxNet: https://github.com/apache/incubator-mxnet.git commit: da5242b732de39ad47d8ecee582f261ba5935fa9, Compiler: gcc 4.8.5,MKL DNN version: v0.17, ResNet 101: https://github.com/apache/incubator-MXNet/blob/master/python/MXNet/gluon/model_zoo/vision/resnet.py ,BS= 64, synthetic Data, 2 instance/2 socket, Datatype:FP32

3.1x performance boost with TensorFlow Inception v3: Tested by Intel as of 3/1/2019. 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:0x4000013),CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: https://hub.docker.com/r/intelaipg/intel-optimized-tensorflow:PR25765-devel-mkl (https://github.com/tensorflow/tensorflow.git commit: 6f2eaa3b99c241a9c09c345e1029513bc4cd470a + Pull Request PR 25765, PR submitted for upstreaming), Compiler: gcc 6.3.0,MKL DNN version: v0.17, Inception v3 : https://github.com/IntelAI/models/tree/master/models/image_recognition/tensorflow/inceptionv3 commit: 87261e70a902513f934413f009364c4f2eed6642 , BS=128, synthetic data, 2 instance/2 socket, Datatype: INT8 vs Tested by Intel as of 3/1/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0286.121520181757, CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: https://hub.docker.com/r/intelaipg/intel-optimized-tensorflow:PR25765-devel-mkl (https://github.com/tensorflow/tensorflow.git commit: 6f2eaa3b99c241a9c09c345e1029513bc4cd470a + Pull Request PR 25765, PR submitted for upstreaming) Compiler: gcc 6.3.0,MKL DNN version: v0.17, Inception v3 : https://github.com/IntelAI/models/tree/master/models/image_recognition/tensorflow/inceptionv3 commit: 87261e70a902513f934413f009364c4f2eed6642 , BS=128, synthetic data, 2 instance/2 socket, Datatype: FP32

2.6x performance boost with PyTorch RetinaNet: Tested by Intel as of 3/1/2019. 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: 0x4000013), Ubuntu 18.04.1 LTS, kernel 4.15.0-45-generic, SSD 1x sda INTEL SSDSC2BA80 SSD 745.2GB, 3X INTEL SSDPE2KX040T7 SSD 3.7TB , Intel® Deep Learning Framework: Pytorch with ONNX/Caffe2 backend: https://github.com/pytorch/pytorch.git (commit: 4ac91b2d64eeea5ca21083831db5950dc08441d6)and Pull Request link: https://github.com/pytorch/pytorch/pull/17464 (submitted for upstreaming), gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, MKL DNN version: v0.17.3 (commit hash: 0c3cb94999919d33e4875177fdef662bd9413dd4), RetinaNet: https://github.com/intel/Detectron/blob/master/configs/12_2017_baselines/retinanet_R-101-FPN_1x.yaml BS=1, synthetic data, 2 instance/2 socket, 0.003mAP accuracy loss, Datatype: INT8 vs Tested by Intel as of 3/1/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 192 GB (12 slots/ 16GB/ 2666 MHz), BIOS: SE5C620.86B.00.01.0015.110720180833 (ucode: 0x200004d), CentOS 7.5, 3.10.0-693.el7.x86_64, Intel® SSD DC S4500 SERIES SSDSC2KB480G7 2.5’’ 6Gb/s SATA SSD 480G, Intel® Deep Learning Framework: Pytorch with ONNX/Caffe2 backend: https://github.com/pytorch/pytorch.git (commit: 4ac91b2d64eeea5ca21083831db5950dc08441d6)and Pull Request link: https://github.com/pytorch/pytorch/pull/17464 (submitted for upstreaming), gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, MKL DNN version: v0.17.3 (commit hash: 0c3cb94999919d33e4875177fdef662bd9413dd4), RetinaNet: https://github.com/intel/Detectron/blob/master/configs/12_2017_baselines/retinanet_R-101-FPN_1x.yaml, BS=1, synthetic data, 2 instance/2 socket, Datatype: FP32

2.5x performance boost with MxNet SSD-VGG16: Tested by Intel as of 3/1/2019. 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:0x4000013),CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: MxNet https://github.com/apache/incubator-mxnet/ -b master da5242b732de39ad47d8ecee582f261ba5935fa9, Compiler: gcc 4.8.5,MKL DNN version: v0.17, SSD-VGG16: https://github.com/apache/incubator-MXNet/blob/master/example/ssd/symbol/vgg16_reduced.py ,BS= 224, Synthetic data, 2 instance/2 socket, 0.0001 mAP accuracy loss , Datatype: INT8 vs Tested by Intel as of 3/1/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0286.121520181757, CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: MxNet https://github.com/apache/incubator-mxnet/ -b master da5242b732de39ad47d8ecee582f261ba5935fa9, Compiler: gcc 4.8.5,MKL DNN version: v0.17, SSD-VGG16: https://github.com/apache/incubator-MXNet/blob/master/example/ssd/symbol/vgg16_reduced.py ,BS= 224, synthetic Data, 2 instance/2 socket, Datatype:FP32

2.2x performance boost with Intel® Optimized Caffe on SSD-Mobilenet v1: 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: 0x4000013), Ubuntu 18.04.1 LTS, kernel 4.15.0-45-generic, SSD 1x sda Intel® SSDSC2BA80 SSD 745.2GB, 3X INTEL SSDPE2KX040T7 SSD 3.7TB , Intel® 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/ssd_mobilenet_int8.prototxt, BS=64, synthetic Data, 2 instance/2 socket, 0.0096 mAP accuracy loss, Datatype: INT8 vs Tested by Intel as of 2/21/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 192 GB (12 slots/ 16GB/ 2666 MHz), BIOS: SE5C620.86B.00.01.0015.110720180833 (ucode: 0x200004d), CentOS 7.5, 3.10.0-693.el7.x86_64, Intel® SSD DC S4500 SERIES SSDSC2KB480G7 2.5’’ 6Gb/s SATA SSD 480G, , Intel® 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/ssd_mobilenet_int8.prototxt, BS=64, synthetic Data, 2 instance/2 socket, Datatype: FP32

2.1x performance boost with TensorFlow on Wide & Deep: Tested by Intel as of 3/1/2019. 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:0x4000013),CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: https://github.com/tensorflow/tensorflow.git 3262818d9d8f9f630f04df23033032d39a7a413 + Pull Request PR26169 + Pull Request PR26261 + Pull Request PR26271 , PR submitted for upstreaming, Compiler:gcc 6.3.1,MKL DNN version: v0.17, Wide & Deep: https://github.com/IntelAI/models/tree/master/benchmarks/recommendation/tensorflow/wide_deep_large_ds commit: a044cb3e7d2b082aebae2edbe6435e57a2cc1f8f ,BS=512, Criteo Display Advertisement Challenge, 2 instance/2 socket, 0.007% accuracy loss, Datatype: INT8 vs Tested by Intel as of 3/1/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0286.121520181757, CentOS 7.6, 4.19.5-1.el7.elrepo.x86_64, Intel® Deep Learning Framework: https://github.com/tensorflow/tensorflow.git 3262818d9d8f9f630f04df23033032d39a7a413 + Pull Request PR26169 + Pull Request PR26261 + Pull Request PR26271 , PR submitted for upstreaming, Compiler:gcc 6.3.1,MKL DNN version: v0.17, Wide & Deep:https://github.com/IntelAI/models/tree/master/benchmarks/recommendation/tensorflow/wide_deep_large_ds a044cb3e7d2b082aebae2edbe6435e57a2cc1f8f, BS= 512, Criteo Display Advertisement Challenge, 2 instance/2 socket,Datatype:FP32

2.1x performance boost with MXNet Wide & Deep: Tested by Intel as of 3/1/2019. 2 socket Intel® Xeon® Platinum 8280L processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2933 MHz), BIOS: SE5C620.86B.0D.01.0348.011820191451 (ucode:0x5000017), CentOS 7.6, Kernel 4.19.5-1.el7.elrepo.x86_64, SSD 1x INTEL SSDSC2KG96 960GB, Intel® Deep Learning Framework: MXNet https://github.com/apache/incubator-mxnet.git commit f1de8e51999ce3acaa95538d21a91fe43a0286ec applying https://github.com/intel/optimized-models/blob/v1.0.2/mxnet/wide_deep_criteo/patch.diff, Compiler: gcc 6.3.1, MKL DNN version: commit: 08bd90cca77683dd5d1c98068cea8b92ed05784, Wide & Deep: https://github.com/intel/optimized-models/tree/v1.0.2/mxnet/wide_deep_criteo commit: c3e7cbde4209c3657ecb6c9a142f71c3672654a5, Dataset: Criteo Display Advertisement Challenge, Batch Size=1024, 2 instance/2 socket, 0.23% accuracy loss, Datatype: INT8 vs Tested by Intel as of 3/1/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2666 MHz), BIOS: SE5C620.86B.0D.01.0286.121520181757 (ucode:0x2000057), CentOS 7.6, Kernel 4.19.5-1.el7.elrepo.x86_64, SSD 1x INTEL SSDSC2KG96 960GB, Intel® Deep Learning Framework: MXNet https://github.com/apache/incubator-mxnet.git commit f1de8e51999ce3acaa95538d21a91fe43a0286ec applying https://github.com/intel/optimized-models/blob/v1.0.2/mxnet/wide_deep_criteo/patch.diff, Compiler: gcc 6.3.1, MKL DNN version: commit: 08bd90cca77683dd5d1c98068cea8b92ed05784, Wide & Deep: https://github.com/intel/optimized-models/tree/v1.0.2/mxnet/wide_deep_criteo commit: c3e7cbde4209c3657ecb6c9a142f71c3672654a5, Dataset: Criteo Display Advertisement Challenge, Batch Size=1024, 2 instance/2 socket, Datatype:FP32

Intel® OpenVINO™ Inference Throughput Performance

AI at the edge is opening up new possibilities in every industry, from predicting machine failures to personalizing retail. With the OpenVINO™ toolkit, businesses can take advantage of near real-time insights to help make better decisions, faster. The OpenVINO™ toolkit allows your business to implement computer vision and deep learning solutions quickly and effectively across multiple applications.

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Configuration Details

2.4x performance boost with OpenVino™ on SqueezeNet v1.1: Tested by Intel as of 1/30/2019. 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:0x4000013), Linux-4.15.0-43-generic-x86_64-with-debian-buster-sid, Compiler: gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, Deep Learning Deployment Toolkit (DLDT): OpenVINO R5.01, SqueezeNet v1.1: https://github.com/opencv/open_model_zoo/blob/master/model_downloader/list_topologies.yml, BS=64, Imagenet, 1 instance/2 socket, Datatype: INT8 vs Tested by Intel as of 1/30/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 192 GB (12 slots/ 16GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0271.120720180605, Linux-4.15.0-29-generic-x86_64-with-Ubuntu-18.04-bionic, Compiler: gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, Deep Learning Deployment Toolkit (DLDT): OpenVINO R5 (DLDTK Version:1.0.19154), SqueezeNet v1.1: https://github.com/opencv/open_model_zoo/blob/master/model_downloader/list_topologies.yml ,Imagenet images , 1 instance/2 socket, Datatype: FP32 (BS=16)

3.1x performance boost with OpenVino™ on MobileNet v1: Tested by Intel as of 1/30/2019. 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:0x4000013), Linux-4.15.0-43-generic-x86_64-with-debian-buster-sid, Compiler: gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, Deep Learning Deployment Toolkit (DLDT): OpenVINO R5.01, MobileNet v1: https://github.com/opencv/open_model_zoo/blob/master/model_downloader/list_topologies.yml, BS=64, Imagenet, 1 instance/2 socket, Datatype: INT8 vs Tested by Intel as of 1/30/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 192 GB (12 slots/ 16GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0271.120720180605, Linux-4.15.0-29-generic-x86_64-with-Ubuntu-18.04-bionic, Compiler: gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, Deep Learning Deployment Toolkit (DLDT): OpenVINO R5 (DLDTK Version:1.0.19154), MobileNet v1: https://github.com/opencv/open_model_zoo/blob/master/model_downloader/list_topologies.yml, Imagenet, 1 instance/2 socket, Datatype: FP32 (BS=16)

3.2x performance boost with OpenVino™ on Inception v4 :Tested by Intel as of 1/30/2019. 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:0x4000013), Linux-4.15.0-43-generic-x86_64-with-debian-buster-sid, Compiler: gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, Deep Learning Deployment Toolkit (DLDT): OpenVINO R5.01, Inception v4: https://github.com/opencv/open_model_zoo/blob/master/model_downloader/list_topologies.yml, BS=128, Imagenet, 1 instance/2 socket, Datatype: INT8 vs Tested by Intel as of 1/30/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 192 GB (12 slots/ 16GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0271.120720180605, Linux-4.15.0-29-generic-x86_64-with-Ubuntu-18.04-bionic, Compiler: gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, Deep Learning Deployment Toolkit (DLDT): OpenVINO R5 (DLDTK Version:1.0.19154), Inception v4: https://github.com/opencv/open_model_zoo/blob/master/model_downloader/list_topologies.yml Imagenet, 1 instance/2 socket, Datatype: FP32 (BS=16)

3.9x performance boost with OpenVino™ ResNet-50: Tested by Intel as of 1/30/2019. 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:0x4000013), Linux-4.15.0-43-generic-x86_64-with-debian-buster-sid, Compiler: gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, Deep Learning Deployment Toolkit (DLDT): OpenVINO R5 (DLDTK Version:1.0.19154 , AIXPRT CP (Community Preview) benchmark (https://www.principledtechnologies.com/benchmarkxprt/aixprt/) BS=64, Imagenet images, 1 instance/2 socket, Datatype: INT8 vs Tested by Intel as of 1/30/2019. 2 socket Intel® Xeon® Platinum 8180 processor, 28 cores HT On Turbo ON Total Memory 192 GB (12 slots/ 16GB/ 2633 MHz), BIOS: SE5C620.86B.0D.01.0271.120720180605, Linux-4.15.0-29-generic-x86_64-with-Ubuntu-18.04-bionic, Compiler: gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0, Deep Learning Deployment Toolkit (DLDT): OpenVINO R5 (DLDTK Version:1.0.19154), AIXPRT CP (Community Preview) benchmark (https://www.principledtechnologies.com/benchmarkxprt/aixprt/) BS=64, Imagenet images, 1 instance/2 socket, Datatype: FP32

Informacje o produktach i wydajności

1

Nawet 30-krotny wzrost przepustowości wnioskowania w przypadku procesora Intel® Xeon® Platinum 9282 z technologią Intel® Deep Learning Boost (Intel® DL Boost): testy przeprowadzone przez firmę Intel 26 lutego 2019 roku. Platforma: Dragon Rock, 2-gniazdowy procesor Intel® Xeon® Platinum 9282 (56 rdzeni na gniazdo), HT WŁ., technologia Turbo WŁ., pamięć całkowita: 768 GB (24 gniazda / 32 GB / 2933 MHz), system BIOS: SE5C620.86B.0D.01.0241.112020180249, CentOS 7, jądro 3.10.0-957.5.1.el7.x86_64, Deep Learning Framework: optymalizacja Intel® dla architektury Caffe*, wersja: https://github.com/intel/caffe d554cbf1, ICC 2019.2.187, wersja MKL DNN: 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, brak warstwy danych syntheticData: 3 × 224 × 224, 56 instancji / 2 gniazda, typ danych: INT8 w porównaniu do testów firmy Intel z 11 lipca 2017 r.: 2-gniazdowy procesor Intel® Xeon® Platinum 8180, 2,50 GHz (28 rdzeni), HT wył., technologia Turbo wył., mechanizm zarządzania skalowaniem ustawiony na „performance” w sterowniku intel_pstate, 384 GB pamięci RAM DDR4-2666 z funkcją ECC. CentOS Linux*, wersja 7.3.1611 (Core), jądro systemu Linux* 3.10.0-514.10.2.el7.x86_64. SSD: Seria dysków Intel® SSD Data Center S3700 (800 GB, 2,5 cala SATA 6 Gb/s, 25 nm, MLC). Wydajność zmierzono przy ustawieniach: zmienne środowiskowe: KMP_AFFINITY='granularity=fine, compact‘, OMP_NUM_THREADS=56, ustawienie częstotliwości procesora: cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Wyciąganie wniosków zmierzono poleceniem „caffe time --forward_only”, szkolenie zmierzono poleceniem „caffe time”. W przypadku topologii „ConvNet” użyto syntetycznego zbioru danych. W przypadku pozostałych topologii dane przechowywano w lokalnej pamięci masowej i buforowano w pamięci operacyjnej przed szkoleniem. Dane techniczne topologii: https://github.com/intel/caffe/tree/master/models/intel_optimized_models(ResNet-50). Kompilator Intel® C++, wer. 17.0.2 20170213, małe biblioteki Intel® Math Kernel Library (Intel® MKL), wersja 2018.0.20170425. Caffe uruchomiono z parametrem „numactl -l”.

2

Wyniki są oparte na testach z dni wskazanych w konfiguracjach i mogą nie uwzględniać wszystkich publicznie dostępnych aktualizacji zabezpieczeń. Więcej informacji zawiera zastrzeżenie dotyczące konfiguracji. Żaden produkt ani komponent nie jest w stanie zapewnić całkowitego bezpieczeństwa.

3

Oprogramowanie i obciążenia wykorzystane w testach wydajności mogły zostać zoptymalizowane pod kątem wydajnego działania tylko na mikroprocesorach Intel®. Testy wydajności, takie jak SYSmark* i MobileMark*, mierzą wydajność określonych systemów komputerowych, komponentów, oprogramowania, operacji i funkcji. Jakakolwiek zmiana wyżej wymienionych czynników może spowodować uzyskanie innych wyników. Aby wszechstronnie ocenić planowany zakup, w tym wydajność danego produktu w porównaniu z konkurencyjnymi, należy zapoznać się z informacjami z innych źródeł oraz innymi testami wydajności. Więcej informacji można znaleźć na stronie www.intel.pl/benchmarks.

4

Informacja o optymalizacji: kompilatory firmy Intel nie zawsze optymalizują w tym samym stopniu procesory innych firm w przypadku tych optymalizacji, które nie są specyficzne dla procesorów Intel®. Optymalizacje te dotyczą między innymi zestawów instrukcji Intel® Streaming SIMD Extensions 2 (Intel® SSE2), Intel® SSE3 oraz Supplemental Streaming SIMD Extensions 3 (SSSE3). Firma Intel nie gwarantuje dostępności, funkcjonalności czy skuteczności każdej optymalizacji w przypadku procesorów wyprodukowanych przez inne firmy. Optymalizacje zależne od procesora dla tego produktu dotyczą wyłącznie procesorów firmy Intel. Niektóre optymalizacje niespecyficzne dla mikroarchitektury Intel® są zarezerwowane dla procesorów Intel. Więcej informacji dotyczących konkretnych zestawów instrukcji omawianych w niniejszym ogłoszeniu można znaleźć w materiałach informacyjnych i podręcznikach użytkownika właściwych dla produktu. Nr wersji informacji: 20110804.