FCDenseNet Benchmarks (#1) · Issues · disMultiABM / stemdl

FCDenseNet Benchmarks

1. Modify stemdl/inputs/DatasetTFRecords to generate batch of inputs+outputs on the fly.
2. Create dummy TFRecords (for relevant inputs+outputs) to assess impact of I/O.
Benchmarks can be quantified using:
1. gpu timeline traces.
2. analytical flops.
3. Model's data processing throughput as a function of ranks.

Important Notes:

Most of the necessary code is already implemented (in stemdl).
Coordinate with Sean T. (in particular, Sean has a binary that forces direct convolutions --> 2x performance).

A Reconstruction of EM data using FCDenseNet looks promising. As such FCDenseNet is a top candidate model to use in a GB run and/or SC'19 paper submission.
Carry out performance (single gpu for flops) and scaling (multiple nodes for communication) studies of FCDenseNet.  
Input sizes from simulation will vary in size, some relevant sizes are [_x_,256,256], _x_=16x16, 32x32, etc...  
Output size from simulation is [256,256].  
Necessary code (to build FCDenseNet) is in stemdl/network
Benchmarks (might) require following code mods:  
* [x] 1.  Modify stemdl/inputs/DatasetTFRecords to generate batch of inputs+outputs on the fly.   
* [x] 2.  Create dummy TFRecords (for relevant inputs+outputs) to assess impact of I/O.  
Benchmarks can be quantified using:  
* [ ] 1.  gpu timeline traces.  
* [ ] 2.  analytical flops.
* [ ] 3.  Model's data processing throughput as a function of ranks.

__Important Notes__:  
1. Most of the necessary code is already implemented (in stemdl).   
2. Coordinate with Sean T. (in particular, Sean has a binary that forces direct convolutions --> 2x performance).

Edited Jan 22, 2019 by Laanait, Nouamane

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FCDenseNet Benchmarks

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