Why is my GPU slower than CPU when training LSTM/RNN models?

Question

My machine has the following spec:

CPU: Xeon E5-1620 v4

GPU: Titan X (Pascal)

Ubuntu 16.04

Nvidia driver 375.26

CUDA tookit 8.0

cuDNN 5.1

I've benchmarked on the following Keras examples with Tensorflow as the backed reference:

SCRIPT NAME                  GPU       CPU
stated_lstm.py               5sec      5sec 
babi_rnn.py                  10sec     12sec
imdb_bidirectional_lstm.py   240sec    116sec
imbd_lstm.py                 113sec    106sec

My gpu is clearly out performing my cpu in non-lstm models.

SCRIPT NAME                  GPU       CPU
cifar10_cnn.py               12sec     123sec
imdb_cnn.py                  5sec      119sec
mnist_cnn.py                 3sec      47sec 

Has anyone else experienced this?

Answer 1

If you use Keras, use CuDNNLSTM in place of LSTM or CuDNNGRU in place of GRU. In my case (2 Tesla M60), I am seeing 10x boost of performance. By the way I am using batch size 128 as suggested by @Alexey Golyshev.

Answer 2

Too small batch size. Try to increase.

Results for my GTX1050Ti:

imdb_bidirectional_lstm.py
batch_size      time
32 (default)    252
64              131
96              87
128             66

imdb_lstm.py
batch_size      time
32 (default)    108
64              50
96              34
128             25

Answer 3

It's just a tip.

Using GPU is powerful when

1. your neural network model is big.
2. batch size is big.

It's what I found from googling.