Can I parallelize this nested for loop? (Python 3.5) -


i found out bottleneck of code following block. n of order 10,000, , l (10,000)^2. rq_func function takes indices (tuples) , returns float v , dictionary sp_dist of {index : probability} format.

is there way can parallelize code? have access cluster computing can use 20 cores @ time , use option.

    r = np.empty((l,))     q = scipy.sparse.lil_matrix((l, n))      traverser = 0        # populate r , q traversing array     s_index in state_indices:         a_index in action_indices:             v, sp_dist = rq_func(s_index, a_index)             r[traverser] = v             sp_index, prob in sp_dist.items():                 q[traverser, sp_index] = prob             traverser += 1


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