Paddle/benchmark

Benchmark

Machine:

• CPU: 12-core Intel(R) Xeon(R) CPU E5-2620 v2 @2.10GHz
• GPU: Tesla K40m
• cuDNN: v5.1
• system: Docker 1.12.1, all platforms are tested in docker environment.

Platforms:

• PaddlePaddle: paddledev/paddle:gpu-devel-v0.9.0a0
• Tensorflow: gcr.io/tensorflow/tensorflow:0.11.0rc0-gpu
• Caffe: kaixhin/cuda-caffe

Several convolutional neural networks and recurrent neural networks are used to test.

Image

Benchmark Model

AlexNet, GoogleNet and a small network used in Caffe.

• AlexNet: but the group size is one.

• GoogleNet: but remove loss1 and loss2 when testing benchmark.

• SmallNet

Single-GPU

• AlexNet: input - 3 * 227 * 227, Time: ms/batch

BatchSize	64	128	256	512
PaddlePaddle	195	334	602	1629
TensorFlow	223	364	645	1235
Caffe	324	627	1232	2513

Notation

All platforms use cuDNN-v5.1. We see that caffe is slower in this experiment, because its workspace limit size of cuDNN-conv interface is 8 * 1024 * 1024, which is smaller in PaddlePaddle and TensorFlow. Note that Caffe will be faster if increasing the workspace limit size.

• GoogletNet: input - 3 * 224 * 224, Time: ms/batch

BatchSize	64	128	256
PaddlePaddle	613	1149	2348
TensorFlow	644	1176	2219
Caffe	694	1364	out of memory

• SmallNet: input - 3 * 32 * 32, Time ms/batch

BatchSize	64	128	256	512
PaddlePaddle	10.463	18.184	33.113	63.039
TensorFlow	9	15	28	59
Caffe	9.373	16.6606	31.4797	59.719

Notation

All the single-GPU experiments in caffe use caffe time to calculate elapsed time, which does not include parameter updating time. However, both PaddlePaddle and TensorFlow experiments contain the parameter updating time. As compared with the total time, this part is relatively little on single machine, we can ignore it.

In Tensorflow, they implement algorithm searching method instead of using the algorithm searching interface in cuDNN.

Multi-GPU: 4 GPUs

• AlexNet, ms / batch

total-BatchSize	128 * 4	256 * 4
PaddlePaddle	347	622
TensorFlow	377	675
Caffe	1229	2435

For example, if total-BatchSize = 128 * 4, the speedup ratio is calculated by

  time_at_1gpu_batch_128 * 4 / time_at_4gpu_total_batch_512 
= (334 * 4)/347 
= 3.85

• GoogleNet, ms / batch

total-BatchSize	128 * 4	256 * 4
PaddlePaddle	1178	2367
TensorFlow	1210	2292
Caffe	2007	out of memory

RNN

We use lstm network for text classfication to test benchmark.

Dataset

• IMDB
• Sequence length is 100. In fact, PaddlePaddle supports training with variable-length sequence, but TensorFlow needs to pad. Thus, we also pad sequence length to 100 in PaddlePaddle in order to compare.
• Dictionary size=30000
• Peephole connection is used in lstmemory by default in PaddlePaddle. It is also configured in TensorFlow.

Single-GPU

LSTM in Text Classification

Testing 2 lstm layer + fc network with different hidden size and batch size.

• Batch size = 64, ms / batch

hidden_size	256	512	1280
PaddlePaddle	83	184	641
TensorFlow	175	280	818

• Batch size = 128, ms / batch

hidden_size	256	512	1280
PaddlePaddle	110	261	1007
TensorFlow	181	361	1237

• Batch size = 256, ms / batch

hidden_size	256	512	1280
PaddlePaddle	170	414	1655
TensorFlow	238	536	1905

Seq2Seq

The benchmark of sequence-to-sequence network will be added later.

Multi GPU: 4 GPUs

LSTM in Text Classification

• hidden_size = 256, ms / batch

batch_size	256	512
PaddlePaddle	90	118
TensorFlow	226	118

• hidden_size = 512, ms / batch

batch_size	256	512
PaddlePaddle	189	268
TensorFlow	297	383

Seq2Seq

The benchmark of sequence-to-sequence network will be added later.