  A comparison between single-cluster and single-spin algorithms is made for
the Ising model in 2 and 3 dimensions. We compare the amount of computer time
needed to achieve a given level of statistical accuracy, rather than the speed
in terms of site updates per second or the dynamical critical exponents. Our
main result is that the cluster algorithms become more efficient when the
system size, $L^d$, exceeds, $L\sim 70$--$300$ for $d=2$ and $L\sim 80$--$200$
for $d=3$. The exact value of the crossover is dependent upon the computer
being used. The lower end of the crossover range is typical of workstations
while the higher end is typical of vector computers. Hence, even for
workstations, the system sizes needed for efficient use of the cluster
algorithm is relatively large.
