  This paper presents novel approaches to parallelizing particle interactions
on a GPU when there are few particles per cell and the interactions are limited
by a cutoff distance. The paper surveys classical algorithms and then
introduces two alternatives that aim to utilize shared memory. The first
approach copies the particles of a sub-box, while the second approach loads
particles in a pencil along the X-axis. The different implementations are
compared on three GPU models using Cuda and Hip. The results show that the
X-pencil approach can provide a significant speedup but only in very specific
cases.
