Weka's PCA is taking too long to run

I am trying to use Weka for feature selection using PCA algorithm.

My original feature space contains ~9000 attributes, in 2700 samples.
I tried to reduce dimensionality of the data using the following code:

AttributeSelection selector = new AttributeSelection();
PrincipalComponents pca = new PrincipalComponents();
Ranker ranker = new Ranker();
selector.setEvaluator(pca);
selector.setSearch(ranker);
Instances instances = SamplesManager.asWekaInstances(trainSet);
try { 
    selector.SelectAttributes(instances);
    return SamplesManager.asSamplesList(selector.reduceDimensionality(instances));
} catch (Exception e ) {
            ...
}

However, It did not finish to run within 12 hours. It is stuck in the method selector.SelectAttributes(instances);.

My questions are: Is so long computation time expected for weka's PCA? Or am I using PCA wrongly?

If the long run time is expected:
How can I tune the PCA algorithm to run much faster? Can you suggest an alternative? (+ example code how to use it)?

If it is not:
What am I doing wrong? How should I invoke PCA using weka and get my reduced dimensionality?

Update: The comments confirms my suspicion that it is taking much more time than expected.
I'd like to know: How can I get PCA in java - using weka or an alternative library.
Added a bounty for this one.