Use joblib memmapping pool (finally :) )

Joblib now supports sharing memory using memmaping (see joblib/joblib#44), which can result in large memory savings when using parallel processing with large read-only memory blocks.

This PR is an update of #99 and will solve #95. To use this feature, the user has to use `mne.set_cache_dir`. Also, the feature is currently only used in the cluster-stats code, where I made some modifications to use block-wise processing to save memory.

Usage example:

    import numpy as np

    from mne.stats import permutation_cluster_1samp_test

    n_subjects = 100
    n_vars = int(1e6)

    rng = np.random.RandomState(42)
    X = rng.randn(n_subjects, n_vars)
    X[:, 400:1000] += 100

    print 'data size: %f MB' % ((8. * X.size) / 2 ** 20)

    import mne
    mne.set_cache_dir('/dev/shm')
    #mne.set_cache_dir(None)

    T_obs, clusters, p_values, H0 = \
        permutation_cluster_1samp_test(X, n_jobs=8, threshold=100.0,
                                    tail=1, n_permutations=32)

On my workstation, without memmaping, this needs ~7GB of RAM, while with memmaping it needs ~1GB.

Note: You need the latest version of joblib from github to benefit from this.