...
https://github-dev.cs.illinois.edu/kindrtnk/DL
Official TF Benchmark System
...
Characteristics
- Instance type: NVIDIA® DGX-1™
- GPU: 8x NVIDIA® Tesla® P100
- OS: Ubuntu 16.04 LTS with tests run via Docker
- CUDA / cuDNN: 8.0 / 5.1
- TensorFlow GitHub hash: b1e174e
- Benchmark GitHub hash: 9165a70
- Build Command:
bazel build -c opt --copt=-march="haswell" --config=cuda //tensorflow/tools/pip_package:build_pip_package - Disk: Local SSD
- DataSet: ImageNet
- Test Date: May 2017
Our System
...
Characteristics (more details in GitHub Repo)
- Instance
...
- type:
...
- IBM
...
- Power9
...
- Hal000,
...
- 8335-GTG
...
- AC922
...
- server
- CPU:
...
- 2x
...
- 20-core
...
- IBM
...
- POWER9
...
- CPU
...
- @
...
- 2.00GHz
- SDRAM:
...
- 512G
...
- DDR4
- GPU:
...
- 4x
...
- NVIDIA®
...
- Tesla®
...
- V100,
...
- 5120
...
- cores,
...
- 16
...
- GB
...
- HBM
...
- 2
- Disk: Local SSD
- OS:
...
- Red
...
- Hat
...
- Enterprise
...
- Linux
...
- Server
...
- release
...
- 7.4
- Python
...
- Distribution:
...
- Anaconda
...
- python
...
- 3.6.2
- CUDA
...
- /
...
- cuDNN:
...
- 9.1/7.0.5
- TensorFLow
...
- Version:
...
- 1.5.0
...
- DataSet:
...
- ImageNet
...
- (synthetic)
- Precision:
...
- floating
...
- point
...
- 32
...
- and
...
- 16
- Test
...
- Date:
...
- Mar
...
- 25
...
- 2018POWER9
...
- (hal000)
The following table is the result of running with the same configurations as the official Tensorflow benchmark mentioned in "Reference" section above:
...