This note presents some experiments on different clusters of SMPs, where both distributed and shared memory parallel programming paradigms can be naturally combined. Although the platforms exhibit the same macroscopic memory organization, it appears that their individual overall performance is closely dependent on the ability of their hardware to efficiently exploit the local shared memory within the nodes. In that context, cache blocking strategy appears to be very important not only to get good performance out of each individual processor but mainly good performance out of the overall computing node since sharing memory locally might become a severe bottleneck. On a very simple benchmark, representative of many large simulation codes, we show through numerical experiments that mixing the two programming models enables us to get attractive speed-ups that compete with a pure distributed memory approach. This opens promising perspectives for smoothly moving large industrial codes developed on distributed vector computers with a moderate number of processors on these emerging platforms for intensive scientific computing that are the clusters of SMPs.