This paper presents the parallelization of a machine learning method, called the adaboost algorithm. The parallel algorithm follows a dynamically load-balanced master-worker strategy, which is parameterized by the granularity of the tasks distributed to workers. We first show the benefits of this version with heterogeneous processors. Then, we study the application in a real, geographically distributed environment, hence adding network latencies to the execution. Performances of the application using more than a hundred processes are analyzed in both JavaSpace and {\pmpi}. We therefore present an head-to-head comparison of two parallel programming models. We study for each case the granularities yielding the best performance. We show that current network technologies enable to obtain interesting speedups in many situations for such an application, even when using a virtual shared memory paradigm in a large-scale distributed environment.