Graph processors such as Graphcore's Intelligence Processing Unit (IPU) are
part of the major new wave of novel computer architecture for AI, and have a
general design with massively parallel computation, distributed on-chip memory
and very high inter-core communication bandwidth which allows breakthrough
performance for message passing algorithms on arbitrary graphs. We show for the
first time that the classical computer vision problem of bundle adjustment (BA)
can be solved extremely fast on a graph processor using Gaussian Belief
Propagation. Our simple but fully parallel implementation uses the 1216 cores
on a single IPU chip to, for instance, solve a real BA problem with 125
keyframes and 1919 points in under 40ms, compared to 1450ms for the Ceres CPU
library. Further code optimisation will surely increase this difference on
static problems, but we argue that the real promise of graph processing is for
flexible in-place optimisation of general, dynamically changing factor graphs
representing Spatial AI problems. We give indications of this with experiments
showing the ability of GBP to efficiently solve incremental SLAM problems, and
deal with robust cost functions and different types of factors.