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[WIP] Parallel decoupled Kalman filter training #61

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[WIP] Parallel decoupled Kalman filter training #61

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Implements decoupled Kalman filter (DEKF) NN training. Different levels of decoupling are possible:

  • No decoupling (GEKF), limiting case of single weight group.
  • Element-wise decoupling (ED-GEKF), see also https://doi.org/10.1021/acs.jctc.5b00211
  • Per-layer decoupling.
  • Per-node decoupling (NDEKF).
  • Per-weight decoupling (full decoupling), limiting case.

@singraber singraber added the enhancement New feature or request label Sep 5, 2020
singraber and others added 9 commits September 6, 2020 00:55
@singraber
Fix examples with new keyword.
dce6dd9
@singraber
Added MPI communicator and decoupling group mask
d0a6af3
@singraber
Started with group mask for layer decoupling, WIP!
31bcd58
@singraber
All decoupling group masks look correct
33719c4
@singraber
Introduced new NN weight memory layout
5c540db 
- Weights and biases grouped by neuron.
- Simplifies node decoupling implementation => block submatrices.
- Old weight ordering available via compile flag
(-DALTERNATIVE_WEIGHT_ORDERING)
- Weight file output remains unchanged.
@singraber
Changed decoupling setup
0235ce5 
- Works now with limits instead of a group mask.
- Groups are assumed to be contiguous.
- Implemented rank 0 only update via new communicator.
@singraber
Decoupling on single processor works
0a95ce8
@singraber
Communicate weight segments correctly
11dc116
Merge branch 'master' of github.com:CompPhysVienna/n2p2 into parallel…
9963d14 
…_dekf
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