  I describe here the performance of a parallel treecode with individual
particle timesteps. The code is based on the Barnes-Hut algorithm and runs
cosmological N-body simulations on parallel machines with a distributed memory
architecture using the MPI message-passing library. For a configuration with a
constant number of particles per processor the scalability of the code was
tested up to P=128 processors on an IBM SP4 machine. In the large $P$ limit the
average CPU time per processor necessary for solving the gravitational
interactions is $\sim 10 %$ higher than that expected from the ideal scaling
relation. The processor domains are determined every large timestep according
to a recursive orthogonal bisection, using a weighting scheme which takes into
account the total particle computational load within the timestep. The results
of the numerical tests show that the load balancing efficiency $L$ of the code
is high ($>=90%$) up to P=32, and decreases to $L\sim 80%$ when P=128. In the
latter case it is found that some aspects of the code performance are affected
by machine hardware, while the proposed weighting scheme can achieve a load
balance as high as $L\sim 90%$ even in the large $P$ limit.
