Symbolic parallelism is a fresh look at the decade-old problem of turning sequential, imperative, code into associative reduction kernels, based on the realization that map/reduce is at its core a staging problem: how can programs be separated so that part of the computation can be done before loop-carried dependencies become available? Previous work has investigated dynamic approaches that build symbolic summaries while the actual data is processed. In this short paper, we approach the problem from the static side, and show that with simple syntax- or type-driven transformations, we can readily transform large classes of imperative groupby-aggregate programs into map/reduce parallelism with deterministic overhead.