libsvm : C++ vs. MATLAB : What's With The Diff

I have two multi-class data sets with 5 labels, one for training, and the other for cross validation. These data sets are stored as .csv files, so they act as a control in this experiment.

I have a C++ wrapper for libsvm, and the MATLAB functions for libsvm.

For both C++ and MATLAB: Using a C-type SVM with an RBF kernel, I iterate over 2 lists of C and Gamma values. For each parameter combination, I train on the training data set and then predict the cross validation data set. I store the accuracy of the prediction in a 2D map which correlates to the C and Gamma value which yielded the accuracy.

I've recreated different training and cross validation data sets many, many times. Each time, the C++ and MATLAB accuracies are different; sometimes by a lot! Mostly MATLAB produces higher accuracies, but sometimes the C++ implementation is better.

What could be accounting for these differences? The C/Gamma values I'm trying are the same, as are the remaining SVM parameters (default).

There should be no significant differences as both C and Matlab codes use the same svm.c file. So what can be the reason?

implementation error in your code(s), this is unfortunately the most probable one
used wrapper has some bug and/or use other version of libsvm then your matlab code (libsvm is written in pure C and comes with python, Matlab and java wrappers, so your C++ wrapper is "not official") or your wrapper assumes some additional default values, which are not default in C/Matlab/Python/Java implementations
you perform cross validation in somewhat randomized form (shuffling the data and then folding, which is completely correct and reasonable, but will lead to different results in two different runs)
There is some rounding/conversion performed during loading data from .csv in one (or both) of your codes which leads to inconsistencies (really not likely to happen, yet still possible)