total memory bandwidth is increased by the number of nodes.

One penalty of parallelising in this way is that each node must calculate the same interim values (e.g. the values of the Hermite polynomials). This duplication can be reduced by partitioning the Taylor terms among fewer nodes, with each node being responsible for a particular row or column of the Taylor terms.

If $q'^2$ is less than the number of nodes, then some nodes will have no work to perform, as they are responsible for Taylor coefficients that are not being updated. One must be careful, therefore, to choose an algorithm parameterisation i.e. ($r$,$p$ and $p_d$) that guarantees never to give $q'^2$ less than the number of nodes.

The efficiency of this approach depends crucially on the properties of the computing nodes. It is useful if the nodes are storage or memory bound, but not if the cost of computing the interim values dominates the computing time. As this tradeoff requires intimate knowledge of the particular computing architecture, we will not pursue a detailed analysis of parallelisation in this paper.

Degridding with the Fast Gauss Transform
----------------------------------------

Degridding is used as part of the major cycle of Cotton-schwab clean [@Rau11]. 