benchmarks.jl

using BenchmarkTools
using KernelFunctions

N1 = 10
N2 = 20

X = rand(N1, N2)
Xc = ColVecs(X)
Xr = RowVecs(X)
Xv = collect.(eachcol(X))
Y = rand(N1, N2)
Yc = ColVecs(Y)
Yr = RowVecs(Y)
Yv = collect.(eachcol(Y))

# Create the general suite of benchmarks
SUITE = BenchmarkGroup()

kernels = Dict(
    "SqExponential" => SqExponentialKernel(), "Exponential" => ExponentialKernel()
)

inputtypes = Dict("ColVecs" => (Xc, Yc), "RowVecs" => (Xr, Yr), "Vecs" => (Xv, Yv))

functions = Dict(
    "kernelmatrixX" => (kernel, X, Y) -> invoke(kernelmatrix, Tuple{kernel, Any}, kernel, X),
    "kernelmatrixXY" => (kernel, X, Y) -> kernelmatrix(kernel, X, Y),
    "kernelmatrix_diagX" => (kernel, X, Y) -> kernelmatrix_diag(kernel, X),
    "kernelmatrix_diagXY" => (kernel, X, Y) -> kernelmatrix_diag(kernel, X, Y),
)

for (kname, kernel) in kernels
    SUITE[kname] = sk = BenchmarkGroup()
    for (inputname, (X, Y)) in inputtypes
        sk[inputname] = si = BenchmarkGroup()
        for (fname, f) in functions
            si[fname] = @benchmarkable $f($kernel, $X, $Y)
        end
    end
end

# Uncomment the following to run benchmark locally

tune!(SUITE)

results = run(SUITE, verbose=true)

Xc = ColVecs(rand(2, 2000))
k = SqExponentialKernel()

@which kernelmatrix(k, Xc)
@btime kernelmatrix($k, $Xc);
@btime invoke(kernelmatrix, Tuple{KernelFunctions.SimpleKernel, $typeof(Xc)}, $k, $Xc);
# @btime invoke(kernelmatrix, Tuple{Kernel, $typeof(Xc)}, $k, $Xc);