System information

• OS Platform and Distribution: Linux Ubuntu 16.04
• TensorFlow installed from (source or binary): source
• TensorFlow version (use command below): 1.3.0
• Python version: 2.7.12
• Bazel version (if compiling from source): 0.5.0
• CUDA/cuDNN version: 8.0/6.0
• GPU model and memory: NVIDIA GTX 1060 / 3GB
• Docker used: yes
• I picked up the code from the word2vec example on your official repo and made a few changes.The core code to train word2vec remains the same.

Describe the problem

I have been working on benchmarking commonly used frameworks/libraries for unsupervised learning of word embeddings(word2vec). I am currently comparing tensorflow(cpu/gpu), gensim, deeplearning4j and the original c code on standard metrics like training time, peak memory usage and quality of learned vectors. Link to my github repo (still working on it). I ran the benchmark on text8 corpus(plan to run it on a much larger corpus later for the true picture) which gave me strange results.

• Tensorflow on GPU is much slower than CPU
• Tensorflow is much slower than other frameworks

Is this behavior expected? Would appreciate any inputs.

Source code / logs

Link to tensorflow code
Link to results of sample benchmark on text8 corpus