  GPU computing is popular due to the calculation potential of a single card.
The N-body integrator GENGA is built to for this, but it suffers a performance
penalty on consumer-grade GPUs due to their truncated double precision (FP64)
performance. We aim to speed up GENGA on consumer-grade cards by harvesting
their high single-precision performance (FP32). We modified GENGA to be able to
compute the long-distance forces between bodies in FP32 precision and tested
this with 5 experiments. We ran simulations with similar initial conditions of
6600 planetesimals in both FP32 and FP64 precision. We also ran simulations
that i) began with a mixture of planetesimals and planetary embryos, ii)
planetesimal-driven giant planet migration, and iii) terrestrial planet
formation with a gas disc. Second, we ran the same simulation beginning with 40
000 planetesimals using both FP32 and FP64 precision forces on a variety of
consumer-grade and Tesla GPUs to measure the performance boost of FP32
computing. There are no statistical differences when running in FP32 or FP64
precision that can be attributed to the force prescription rather than
stochastic effects. The uncertainties in energy are almost identical when using
both precisions. However, the uncertainty in the angular momentum using FP32
rather than FP64 precision long-range forces is about two orders of magnitude
greater, but still very low. Running the simulations in single precision on
consumer-grade cards decreases running time by a factor of three and becomes
within a factor of three of a Tesla A100 GPU. Additional tuning speeds up the
simulation by a factor of two across all types of cards. The option to compute
the long-range forces in single precision in GENGA when using consumer-grade
GPUs dramatically improves performance at a little penalty to accuracy. There
is an additional environmental benefit because it reduces energy usage.
