While the distribution-free nature of permutation tests makes them the most appropriate method for hypothesis testing under a wide range of conditions, their computational demands can be runtime prohibitive, especially if samples are not very small and/or many tests must be conducted (e.g. all pairwise comparisons). This paper presents statistical code that performs continuous-data permutation tests under such conditions very quickly – often more than an order of magnitude faster than widely available commercial alternatives when many tests must be performed and some of the sample pairs contain a large sample. Also presented is an efficient method for obtaining a set of permutation samples containing no duplicates, thus maximizing the power of a pairwise permutation test under a conventional Monte Carlo approach with negligible runtime cost (well under 1% when runtimes are greatest). For multiple comparisons, the code is structured to provide an additional speed premium, making permutation-style p-value adjustments practical to use with permutation test p-values (although for relatively few comparisons at a time). “No-replacement” sampling also provides a power gain for such multiple comparisons, with similarly negligible runtime cost.