Fuzzy relations are simple mathematical structures that enable a very general representation of fuzzy knowledge, and fuzzy relational calculus offers a powerful machinery for approximate reasoning.However, one of the most relevant limitations of approximate reasoning is the efficiency bottleneck.In this paper, we present two implementations for fast fuzzy inference through relational composition, with the twofold objective of being general and efficient.The two implementations are capable of working on full and sparse representations respectively.Further, a wrapper procedure is capable of automatically selecting the best implementation on the basis of the input features.We implemented the code in GNU Octave because it is a high-level language targeted to numerical computations.Experimental results show the impressive performance gain when the proposed implementation is used.