Project roadmap

Publication venues:

• ICML (deadline January)
• Climate Informatics (deadline probably Feburary)

Proposed paper contents:

• Focus on algorithm that supports 3D grids but perhaps not time?
• Derivation of the algorithm (roughly following So's Overleaf)
• "Clever bits" of GPU implementation
• Demonstrations on simulated data
• Large scale application to real data

Tasks

Each task has a priority: P1 = high priority, P4 = low priority

I've then ordered the themes roughly in order of decreasing priority based on the tasks they contain.

Improve the INLA integration

• [P2] Why does it converge to something different to MP and 3D-Var?
  • Noise currently isn't set correctly (I think).
• [P3] Directly use solve functions suggested by INLA people rather than main INLA method, to reduce overheads
• [P4] Avoid writing the observations to disk when transferring between Python and R

Large scale applications for 2D GP

• [P3] Implement multi-GPU

Use MC method for the variance

Possibly following "Efficient Covariance Approximations for Large Sparse Precision Matrices"

• [P4] Can we use the output of this method as an input to the message passing to speed up convergece?
• [P4] Reimplement Marc's code from Slack in JAX
• [P4] Look at work of Sharana Kumar Shivanand who gave presentation on this to F-X's group meeting

Improve the convergence speed of the mean

• [P4] Try Aitken's and Steffenson’s method from this paper
• [P4] Try randomised message scheduling from this paper

Investigate and write up why we can't choose good cs