I'm not 100% sure about how HPSVM calculates these numbers, but here is some information on the number of support vectors in general.
In a linear model, if the data is completely separable then the number of support vectors will equal the number of the support vectors on the margin. Being completely separable means that you can draw the hyperplane to completely separate the two classes.
In general the data is more mixed, and thus there is no hyperplane that will completely separate the two classes. Thus, the penalty parameter exists - to modify the optimization (This is the proc option C in HPSVM). At this point the number of support vectors are the number of vectors on the margin (both sides of the hyperplane), AND all the vectors in between the two margins. Thus there will be more support vectors, than the number of support vectors on the margin because the vectors in between.
Adjusting the penalty (proc option C) can adjust the number of support vectors in between the margins, but this also has an effect on the accuracy of the model.
I think there are some extra nuances with non-linear models and the number of support vectors, but the general idea still holds.
Hopefully this helps.
