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add graph in IntelOptimizedReadme
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benchmark/IntelOptimizedPaddle.md

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Machine:
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- Server
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- Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
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- Laptop
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- DELL XPS15-9560-R1745: i7-7700HQ 8G 256GSSD
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- i5 MacBook Pro (Retina, 13-inch, Early 2015)
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- Desktop
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- i7-6700k
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- Server: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
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- Laptop: TBD
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System: CentOS release 6.3 (Final), Docker 1.12.1.
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PaddlePaddle: paddlepaddle/paddle:latest (for MKLML and MKL-DNN), paddlepaddle/paddle:latest-openblas (for OpenBLAS)
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- MKL-DNN tag v0.11
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- MKLML 2018.0.1.20171007
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- OpenBLAS v0.2.20
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(TODO: will rerun after 0.11.0)
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PaddlePaddle: (TODO: will rerun after 0.11.0)
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- paddlepaddle/paddle:latest (for MKLML and MKL-DNN)
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- MKL-DNN tag v0.11
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- MKLML 2018.0.1.20171007
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- paddlepaddle/paddle:latest-openblas (for OpenBLAS)
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- OpenBLAS v0.2.20
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On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
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| MKLML | 12.12 | 13.70 | 16.18 |
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| MKL-DNN | 28.46 | 29.83 | 30.44 |
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chart on batch size 128
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TBD
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<img src="figs/vgg-cpu-train.png" width="500">
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- ResNet-50
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| MKLML | 32.52 | 31.89 | 33.12 |
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| MKL-DNN | 81.69 | 82.35 | 84.08 |
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chart on batch size 128
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TBD
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<img src="figs/resnet-cpu-train.png" width="500">
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- GoogLeNet
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| MKLML | 128.46| 137.89| 158.63 |
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| MKL-DNN     | 250.46| 264.83| 269.50 |
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chart on batch size 128
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TBD
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<img src="figs/googlenet-cpu-train.png" width="500">
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### Laptop
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TBD
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### Desktop
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TBD
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benchmark/figs/resnet-cpu-train.png

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benchmark/figs/vgg-cpu-train.png

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