Why are multiple-gpu slower than single gpu

[YOUYOUYOU]

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System information

• What is the top-level directory of the model you are using:
• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
• TensorFlow installed from (source or binary):
• TensorFlow version (use command below):
• Bazel version (if compiling from source):
• CUDA/cuDNN version:
• GPU model and memory:
• Exact command to reproduce:

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https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh

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python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"

Describe the problem

Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.

Source code / logs

Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem. I trained textsum at 4-gpus and 1-gpu ,I found 4-gpus is slower than single gpu , and they all have low utilization, anyone can help me resolve this problem?

[k-w-w]

Make sure that you are comparing number of batches that are trained per step -- 4 GPUs could mean that the model is training 4x the number of batches each step, so each step could take longer but more examples are trained.

This question is better asked on StackOverflow since it is not a bug or feature request. There is also a larger community that reads questions there.

If you think we've misinterpreted a bug, please comment again with a clear explanation, as well as all of the information requested in the issue template.