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2020

Kernel Operations on the GPU, with Autodiff, without Memory Overflows

Charlier, Benjamin, Feydy, Jean, Glaunès, Joan Alexis et al.

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The KeOps library provides a fast and memory-efficient GPU support for tensors whose entries are given by a mathematical formula, such as kernel and distance matrices.

KeOps alleviates the major bottleneck of tensor-centric libraries for kernel and geometric applications: memory consumption.
It also supports automatic differentiation and outperforms standard GPU baselines, including PyTorch CUDA tensors or the Halide and TVM libraries.
KeOps combines optimized C++/CUDA schemes with binders for high-level languages: Python (Numpy and PyTorch), Matlab and GNU R.

Kernel Operations on the GPU, with Autodiff, without Memory Overflows · Around

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SciPy: Open source scientific tools for Python, 2001
E. Jones, T. Oliphant, P. Peterson, et al · 2001
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Gpu Gems 3
H. Nguyen · 2007
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Nyström method vs random Fourier features: A theoretical and empirical comparison
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T. Yang, Y.-F. Li, M. Mahdavi, R. Jin, and Z.-H. Zhou · 2012
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, et al · 2015
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