  The using of GPU for Monte Carlo particle transport is lacking of fair
comparisons. This work performs simulations on both CPU and GPU in the same
package under the same manufacturing process of low power mobile devices. The
experiment with simple pincell benchmark problems with fresh fuel gives
consistent results between CPU and GPU. In the meanwhile, it finds that the
Apple M1 GPU is as twice capable as M1 CPU, while entitled with a 5 times
advantage in power consumption. The particle sorting algorithm optimized for
GPU improves computing efficiency by 28\%, while prominently reducing GPU power
consumption. Such advantage of sorting algorithm is expected to be greater for
depleted fuel problems than fresh fuel problem. The kernel reconstruction
Doppler broadening algorithm designed for continuously varying materials is
demonstrated to produce consistent Doppler coefficients with the reference code
and the algorithm can be efficiently implemented on GPU. Compared with the
reference code with double precision floating point numbers, the testing codes
with single precision floating point numbers could underestimate the
K-effective values by about 500 pcm, and the Doppler coefficients of the fuel
are well reproduced though. The conclusion may strengthen the argument that it
is helpful for high performance computer to adopt GPU in order to reduce gross
power consumption.
