I agree with kbumsik here. AMD only has themselves to blame. They have great hardware and fantastic theoretical benchmarks. Heck, even their SGEMMs are really fast and in line with the 15TFlops of FP32 on the VEGA 64s that we've benchmarked. However, it comes down to software ecosystem and optimizations for common deep learning subroutines.
MIOpen[1] is a step in this direction but still causes the VEGA 64 + MIOpen to be 60% of the performance of a 1080 Ti + CuDNN based on benchmarks we've conducted internally at Lambda. Let that soak in for a second: the VEGA 64 (15TFLOPS theoretical peak) is 0.6x of a 1080 Ti (11.3TFLOPS theoretical peak). MIOpen is very far behind CuDNN.
Lisa Su, if you're reading this, please give the ROCm team more budget!
Or, maybe, a new team.
Re:2080 Ti TensorFlow GPU Benchmarks
by montalbano on Thursday October 11, 2018 @07:19PM
(#18197822)
> No, AMD gpu is zero cost effective because Tensorflow does not support AMD gpus.
Incorrect, see:
No, AMD gpu is zero cost effective because Tensorflow does not support AMD gpus.
> But people are locked in the Nvidia proprietary jail and no one seems to care...
Sounds like you want to blame the users, but this is because Nvidia highly invested on GPGPU and Cuda since more than 10 years ago, while AMD did focus on something else like HSA. It is AMD’s fault.