High energy physic scientists are strongly concerned by numerical simulations and High Performance Computing. In the field of numerical simulation, particle transport is known to be a hard problem especially when Monte Carlo solvers are used. These applications are difficult to parallelize and optimize due to the non determinism of the Monte Carlo approach. These simulations have to manage the transport of several billions of particles. Application developers have also to deal with memory consumption in order to fit into the memory available per computing node and they have to balance the workload among clusters nodes.

Our contribution consists mainly of a new algorithm dedicated to particle Monte Carlo transport able to solve the issues of memory consumption and load balancing. The computing load is modeled with a weighted graph to abstract the objects of simulated physical system. This load balancing algorithm is studied on a benchmark set and the performance results using 4096 CPU cores are very promising. Comparing to the fully replicated perfectly balanced approach, our algorithm provides a significant memory footprint reduction while keeping the imbalance between computing nodes below 15% and finally obtains a better parallel efficiency.