AbstractMotivationModality matching in single-cell omics data analysis—i.e. matching cells across datasets collected using different types of genomic assays—has become an important problem, because unifying perspectives across different technologies holds the promise of yielding biological and clinical discoveries. However, single-cell dataset sizes can now reach hundreds of thousands to millions of cells, which remain out of reach for most multimodal computational methods.ResultsWe proposeLSMMD-MA, a large-scale Python implementation of theMMD-MAmethod for multimodal data integration. InLSMMD-MA, we reformulate theMMD-MAoptimization problem using linear algebra and solve it with KeOps, a CUDA framework for symbolic matrix computation in Python. We show thatLSMMD-MAscales to a million cells in each modality, two orders of magnitude greater than existing implementations.Availability and implementationLSMMD-MAis freely available athttps://github.com/google-research/large_scale_mmdmaand archived athttps://doi.org/10.5281/zenodo.8076311.