Paddle/benchmark/IntelOptimizedPaddle.md

Benchmark

Machine:

• Server
  • Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
• Laptop
  • DELL XPS15-9560-R1745: i7-7700HQ 8G 256GSSD
  • i5 MacBook Pro (Retina, 13-inch, Early 2015)
• Desktop
  • i7-6700k

System: CentOS release 6.3 (Final), Docker 1.12.1.

PaddlePaddle: paddlepaddle/paddle:latest (for MKLML and MKL-DNN), paddlepaddle/paddle:latest-openblas (for OpenBLAS)

• MKL-DNN tag v0.11
• MKLML 2018.0.1.20171007
• OpenBLAS v0.2.20 (TODO: will rerun after 0.11.0)

On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.

Benchmark Model

Server

Test on batch size 64, 128, 256 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz

Input image size - 3 * 224 * 224, Time: images/second

• VGG-19

BatchSize	64	128	256
OpenBLAS	7.80	9.00	10.80
MKLML	12.12	13.70	16.18
MKL-DNN	28.46	29.83	30.44

chart on batch size 128 TBD

• ResNet-50

BatchSize	64	128	256
OpenBLAS	25.22	25.68	27.12
MKLML	32.52	31.89	33.12
MKL-DNN	81.69	82.35	84.08

chart on batch size 128 TBD

• GoogLeNet

BatchSize	64	128	256
OpenBLAS	89.52	96.97	108.25
MKLML	128.46	137.89	158.63
MKL-DNN	250.46	264.83	269.50

chart on batch size 128 TBD

Laptop

TBD

Desktop

TBD