Throughput of Sparse Matrix-Matrix Multiply

using PyPlot
using Statistics
using SparseArrays

function sparse_mmpy_test(n::Int64, num_runs::Int64)
    x = randn(n,n);
    y = randn(n,n);
    
    t_dense = mean([(@elapsed x * y) for i = 1:num_runs]);
    
    dds = [0.0003,0.001,0.003,0.01,0.03,0.1,0.3];
    t_sparse_ux = zeros(length(dds));
    t_sparse_xu = zeros(length(dds));
    for i = 1:length(dds)
        dd = dds[i];
        u = sprandn(n,n,dd);
        t_sparse_ux[i] = mean([(@elapsed u * x) for i = 1:num_runs]);
        t_sparse_xu[i] = mean([(@elapsed x * u) for i = 1:num_runs]);
    end
    
    figure(figsize=(5.0,3.2));
    loglog(dds, t_sparse_ux; label="sparse CSC * dense");
    loglog(dds, t_sparse_xu; label="dense * sparse CSC");
    loglog(dds, [t_dense for dd in dds]; label="dense");
    title("Runtime of $n x $n matrix-matrix multiply");
    xlabel("density of u");
    ylabel("runtime (s)");
    legend();
end

sparse_mmpy_test (generic function with 1 method)

sparse_mmpy_test(500, 10);

sparse_mmpy_test(1000, 10);

sparse_mmpy_test(2000, 10);