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Turbo-GMM: FlashAttention-Inspired GPU-Accelerated Gaussian Mixture Model
arXiv:2611.02449v1 Announce Type: new Abstract: Expectation-maximization (EM) for Gaussian mixture models (GMMs) requires scoring $N$ data points against $K$ mixture components. Prior fused GPU implementations, notably Flash-GMM, eliminate materialization of the $N \times K$ responsibility matrix but traverse the component dimension twice to compute normalization terms and accumulate sufficient statistics. We introduce Turbo-GMM, a single-sweep fused EM kernel that completes responsibility normalization and sufficient-statistic accumulation within one traversal of the component dimension. By retaining the complete component axis on chip, Turbo-GMM avoids the second score computation required by the released two-sweep Flash-GMM implementation. Its applicability is limited by register pressure, occupancy, and the component and feature dimensions, making it particularly suitable for large datasets with relatively small numbers of components. For larger configurations, Turbo-GMM employs a two-pass fallback following the established fused design. We establish algebraic equivalence to standard EM in real arithmetic and evaluate sixteen configurations on an NVIDIA A100. Under the reported benchmarking protocol, Turbo-GMM achieves a median $2.15\times$ speedup over the evaluated Flash-GMM implementation and up to $5.3\times$ and $9.9\times$ over PyTorch and \texttt{scikit-learn}, respectively. The results demonstrate the potential of single-sweep component traversal while identifying on-chip resource constraints as the principal limitation of this approach.
Abstract:Expectation-maximization (EM) for Gaussian mixture models (GMMs) requires scoring $N$ data points against $K$ mixture components. Prior fused GPU implementations, notably Flash-GMM, eliminate materialization of the $N \times K$ responsibility matrix but traverse the component dimension twice to compute normalization terms and accumulate sufficient statistics. We introduce Turbo-GMM, a single-sweep fused EM kernel that completes responsibility normalization and sufficient-statistic accumulation within one traversal of the component dimension. By retaining the complete component axis on chip, Turbo-GMM avoids the second score computation required by the released two-sweep Flash-GMM implementation. Its applicability is limited by register pressure, occupancy, and the component and feature dimensions, making it particularly suitable for large datasets with relatively small numbers of components. For larger configurations, Turbo-GMM employs a two-pass fallback following the established fused design. We establish algebraic equivalence to standard EM in real arithmetic and evaluate sixteen configurations on an NVIDIA A100. Under the reported benchmarking protocol, Turbo-GMM achieves a median $2.15\times$ speedup over the evaluated Flash-GMM implementation and up to $5.3\times$ and $9.9\times$ over PyTorch and \texttt{scikit-learn}, respectively. The results demonstrate the potential of single-sweep component traversal while identifying on-chip resource constraints as the principal limitation of this approach.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC)
arXiv:2611.02449 [cs.LG]
(or arXiv:2611.02449v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.02449 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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