Q:

Computer crashes while training with tensorflow on multiple GPUs

My servers crash and automatically restart while training with tensorflow using more than one GPUs.
My task is like:
CUDA_VISIBLE_DEVICES=0 python train.py [LSTM training]
CUDA_VISIBLE_DEVICES=1 python train.py [Another LSTM training]

When the tasks are both running, the server crashes and automatically restarts. I tried with different servers, but all servers behave the same.
My hardware configuration:
Dell T7610 Server
2* E5-2609 CPU
1* Nvidia GTX 1080 (Inno 3D Foundation Edition)
1* Nvidia GTX 1080Ti (Gigabyte Foundation Edition)
64G memory  
My software configuration:
Ubuntu 14.04 LTS (Linux 3.13.0-87-generic)
Nvidia Driver version 378.13 (nvidia-378 from ppa:graphics-drivers)
CUDA 8.0 (CuDNN 5.1.5)
Python 2.7.13 |Anaconda 4.3.0 (64-bit)
tensorflow-gpu-1.0.1 (from pip install)  
The PSU has enough capacity for the graphics cards. And from memtest results, it's not a memory-related problem. There are not any syslog and kern.log log that helps to locate the problem. 
I am aware of a similar question. However, a workaround that downgrades the Linux kernel is unacceptable since I share the machines with lab folks.

A:

Now I've solved the problem. I decided to answer my own question.
It's been a power issue. When the GPU draws power more than the PSU can provide, the computer restarts. Although the PSU has sufficient capacity, a 18A PCIe cable has a power limit of 217W.
My workaround is to use two PCIe power cable to power the GTX1080 with a double 6-pins to 8-pins connector.

