Flash-SD-KDE: Accelerating SD-KDE with Tensor Cores
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Abstract (arXiv)
Score-debiased kernel density estimation (SD-KDE) achieves improved asymptotic convergence rates over classical KDE, but its use of an empirical score has made it significantly slower in practice. We show that by re-ordering the SD-KDE computation to expose matrix-multiplication structure, Tensor Cores can be used to accelerate the GPU implementation. On a 32k-sample 16-dimensional problem, our approach runs up to 47× faster than a strong SD-KDE GPU baseline and 3,300× faster than scikit-learn's KDE. On a larger 1M-sample 16-dimensional task evaluated on 131k queries, Flash-SD-KDE completes in 2.3 s on a single GPU, making score-debiased density estimation practical at previously infeasible scales.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 10 Feb 2026.
Builds on
8 of 26 references.
References · 8
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
- 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,611
- 2017Mixed Precision Trainingcited by 1,135
- 2020Kernel Operations on the GPU, with Autodiff, without Memory Overflowscited by 101
- 2017Gradient Estimators for Implicit Modelscited by 58
- 2025SD-KDE: Score-Debiased Kernel Density Estimationcited by 2
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Citing papers per year: 2026: 1.
1 paper.