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 ( \lower 2.15 pt \hbox { $ \buildrel > \over { \sim } $ } 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 .