ScalarKernelFunctions.jl

This package implements kernel functions that are optimized for one-dimensional input and that are GPU-compatible by default.

This package expands the KernelFunctions.jl package (which is automatically loaded and reexported) by a new abstract type ScalarKernel. Its subtypes include a couple of base kernels with the Scalar prefix. On 1-dimensional inputs, they give exactly the same output as their KernelFunctions.jl counterparts, e.g.:

using ScalarKernelFunctions

k1 = ScalarSEKernel() # from this package
k2 = SEKernel() # from KernelFunctions.jl
x = rand(100)
kernelmatrix(k1, x) ≈ kernelmatrix(k2, x) # true

When combining subtypes of ScalarKernel using +, with_lengthscale, another subtype of ScalarKernel will be produced, which means that specialized implementations will be used for the composite kernel as well. Mixing specialized and "normal" kernels will also work, but will no longer use the specialized implementation.

Specializing on 1d input allows to achieve lower allocation counts and faster evaluation, especially combined with AD packages such as Zygote or Enzyme. Parameter fields are also scalar, which saves allocations with repeated construction of the kernel, e.g. when kernel parameters are being optimized.

The kernels in this package are implemented using broadcast, which allows them to work on the GPU by default. We also export a gpu function, which converts any kernel to use Float32 parameters (where needed), and calling kernelmatrix will preserve Float32 to be most efficient on GPUs. For example, this is how we use a kernel on CuArrays:

using CUDA
x = CUDA.rand(100)
k = ScalarPeriodicKernel() |> gpu # ScalarPeriodicKernel{Float32}(1.0f0)
kernelmatrix(k, x) # returns 100×100 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}

Omitting the gpu conversion will of course also work, but will be quite a bit slower.

• ScalarSEKernel
• ScalarLinearKernel
• ScalarPeriodicKernel
• ScalarMatern12Kernel === ScalarExponentialKernel
• ScalarMatern32Kernel
• ScalarMatern52Kernel
• ScalarMaternKernel

• ScalarKernelSum, when doing k1 + k2, where k1 and k2 are ScalarKernels
• ScalarKernelProduct
• ScalarTransformedKernel, when doing k ∘ t, where k is a ScalarKernel and t is a Transform
• ScalarScaledKernel, when doing a * k, where k is a ScalarKernel and a is a Real

• ScalarScaleTransform