Hello,
We were able to reproduce and root cause the issue you were seeing. With CUDA 10 you should be able to see a performance increase in TitanV for similar workloads. The team at Nvidia understands the underlying concern that you’ve raised about locking GPU clocks while in Compute mode.
The Titan V was designed to deliver consistently accurate compute results and it will do so in many desktop environments. As a result, we set a conservative clock when running CUDA workloads. For users who want to push the TitanV past our spec, we’ll be enabling overclocking with a driver update that we’re aiming to post in November. In addition, we wanted you to note that the key advantage of TitanV over 1080Ti is the Tensor Cores.
Here are some examples, tools, and info that you can take a look at to fully take advantage of the Tensor Cores on TitanV.
Examples
· New mixed-precision model examples: https://developer.nvidia.com/deep-learning-examples
· GitHub: GitHub - NVIDIA/DeepLearningExamples: Deep Learning Examples
· TensorFlow mixed-precision video: NVIDIA Developer How To Series: Mixed-Precision Training - YouTube
Tools
· TensorFlow OpenSeq2seq: Mixed Precision Training — OpenSeq2Seq 0.2 documentation & arVix paper
Further information
· Mixed-precision blog: https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/
· Mixed-precision best practices: https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
· Mixed-precision arVix paper: [1710.03740] Mixed Precision Training
Please let us know if you have any questions.