We show that a broad class of $(+,\diamond)$ vector products (for binary integer functions $\diamond$) are equivalent under one-to-polylog reductions to the computation of the Hamming distance. Examples include: the dominance product, the threshold product and $\ell_{2p+1}$ distances for constant $p$. Our results imply equivalence (up to polylog factors) between complexity of computation of All Pairs: Hamming Distances, $\ell_{2p+1}$ Distances, Dominance Products and Threshold Products. As a consequence, Yuster's~(SODA'09) algorithm improves not only Matoušek's (IPL'91), but also the results of Indyk, Lewenstein, Lipsky and Porat (ICALP'04) and Min, Kao and Zhu (COCOON'09). Furthermore, our reductions apply to the pattern matching setting, showing equivalence (up to polylog factors) between pattern matching under Hamming Distance, $\ell_{2p+1}$ Distance, Dominance Product and Threshold Product, with current best upperbounds due to results of Abrahamson (SICOMP'87), Amir and Farach (Ann.~Math.~Artif.~Intell.'91), Atallah and Duket (IPL'11), Clifford, Clifford and Iliopoulous (CPM'05) and Amir, Lipsky, Porat and Umanski (CPM'05). The resulting algorithms for $\ell_{2p+1}$ Pattern Matching and All Pairs $\ell_{2p+1}$, for $2p+1 = 3,5,7,\dots$ are new. Additionally, we show that the complexity of AllPairsHammingDistances (and thus of other aforementioned AllPairs- problems) is within polylog from the time it takes to multiply matrices $n \times (n\cdot d)$ and $(n\cdot d) \times n$, each with $(n\cdot d)$ non-zero entries. This means that the current upperbounds by Yuster (SODA'09) cannot be improved without improving the sparse matrix multiplication algorithm by Yuster and Zwick~(ACM TALG'05) and vice versa.