Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on recent developments in machine learning hardware. We present an efficient and general approach to GP inference based on Blackbox Matrix-Matrix multiplication (BBMM). BBMM inference uses a modified batched version of the conjugate gradients algorithm to derive all terms required for training and inference in a single call. Adapting this algorithm to complex models simply requires a routine for efficient matrix-matrix multiplication with the kernel and its derivative. In addition, BBMM utilizes a specialized preconditioner that substantially speeds up convergence. In experiments, we show that BBMM efficiently utilizes GPU hardware, speeding up GP inference by an order of magnitude on a variety of popular GP models. Additionally, we provide GPyTorch, a new software platform for scalable Gaussian process inference via BBMM, built on PyTorch.

本研究提出了一种基于黑盒矩阵乘法（BBMM）的高斯过程推理方法，该方法可将精确GP推理的渐近复杂度从$O(n^3)$降至$O(n^2)$，并成功地使用GPU硬件显著加速了推理过程，此外还提供了一种基于PyTorch的软件平台GPyTorch，可用于进行可扩展的高斯过程推理。

GPyTorch：具有 GPU 加速的黑盒矩阵-矩阵高斯过程推理