Massively parallel approximate Gaussian process regression

Understand

We explore how the big-three computing paradigms -- symmetric multi-processor (SMC), graphical processing units (GPUs), and cluster computing -- can together be brought to bare on large-data Gaussian processes (GP) regression problems via a careful implementation of a newly developed local approximation scheme.

• Our methodological contribution focuses primarily on GPU computation, as this requires the most care and also provides the largest performance boost.
• However, in our empirical work we study the relative merits of all three paradigms to determine how best to combine them.
• The paper concludes with two case studies.