Apache* MXNet community announced the v1.2.0 release of the Apache MXNet deep learning framework. One of the most important features in this release is the Intel optimized CPU backend: MXNet now integrates with Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) to accelerate neural network operators: Convolution, Deconvolution, FullyConnected, Pooling, Batch Normalization, Activation, LRN, Softmax, as well as some common operators: sum and concat. More details are available in the release note and release blog. This article will give more details on how to play it and how much faster v1.2.0 is on CPU platform.
In the deployment environment, the latency always is sensitive so the more specific optimizations are applied to reduce the latency for the better real-time results, especially for the batchsize one.
As the following chart shows, the latency of single picture inference (batchsize one) is significantly decreased.
Figure 1. NOTE: the latency can be calculated by (1000 * batchsize / throughput) and the unit is ms.
For the big batchsize, such as BS=32, the throughput has been improved a lot with Intel optimized backend.
As the following chart shows, the throughput of batchsize=32 is about 23.4-56.9X faster than the original CPU backend.
The new backend shows the good scalability for the batchsize. In below chart, the throughput keeps constant at approximately eight images/second for the original CPU backend.
The new implementation shows very good batch scalability where the throughput is boosted from 83.7 images/second (BS=1) to 199.3 images/second (BS=32) for the resnet-50.
CMD to reproduce the results:
export KMP_AFFINITY=granularity=fine,compact,1,0 export vCPUs=`cat /proc/cpuinfo | grep processor | wc -l` export OMP_NUM_THREADS=$((vCPUs / 2))
$ sudo apt-get update $ sudo apt-get install -y wget python gcc $ wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py
MXNet with Intel MKL-DNN backend has been released in 1.2.0.
$ pip install mxnet-mkl==1.2.0 [–user]
Please note that the mxnet-mkl package is built with USE_BLAS=openblas. If you want to leverage the performance boost from MKL blas, please try to install mxnet from source.
$ pip install mxnet==1.2.0 [–user]
$ git clone --recursive https://github.com/apache/incubator-mxnet $ cd incubator-mxnet $ git checkout 1.2.0 $ git submodule update --init --recursive
$ make -j USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl
Note 1: When calling this command, Intel MKL-DNN will be downloaded and built automatically.
Note 2: MKL2017 backend has been removed from MXNet master branch. So users cannot build MXNet with MKL2017 backend from source code anymore.
Note 3: To use MKL as BLAS library, users may need to install Intel® Parallel Studio for best performance.
Note 4: If MXNet cannot find MKLML libraries, please add the MKLML library path to LD_LIBRARY_PATH and LIBRARY_PTH at first.
|CPU/GPU Model, Core, Socket#||Intel® Xeon® Platinum 8180, 56, 2S|
|CPU/GPU TFLOPS(FP32)||8.24T = 2.3G*56*64(AVX512)|
|CPU Config||Turbo on, HT on, NUMA on|
|RAM Bandwidth||255GB/s = 2.66*12*8(2666MHz DDR4)|
|RAM Capacity||192G = 16G*12*1|
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