How can I run SVM on 500k rows with 81 columns?
If you want to stick to SVC(sklearn) , you can use Ensembling
we have many ways of ensembling 
good guide is here : http://mlwave.com/kaggle-ensembling-guide/
for your problem you can train multiple SVC models on different subset of your data,and as quick solution you can combine them whit BaggingClassifier like this : 

clf = BaggingClassifier(SVC(C=1.0,
        cache_size=200,
        class_weight=None,
        coef0=0.0,
        decision_function_shape=None,
        degree=3,
        gamma='auto',
        kernel='rbf',
        max_iter=-1,
        probability=False,
        random_state=None,
        shrinking=True,
        tol=0.001,
        verbose=False,
        ))


If you want to use each record is used only once for training in the BaggingClassifier, set the bootstrap parameter to False.
now you can parallel each of this models in on core of your cluster.