Benchmark MadNLP's kernels

benchmark_kernels.jl

using BenchmarkTools
using CUTEst
using MadNLP

name = "OPTMASS"
nlp = CUTEst.CUTEstModel(name)
solver = MadNLP.MadNLPSolver(nlp)

println("Initialization")
@info "set_initial_rhs!"
@btime MadNLP.set_initial_rhs!(solver, solver.kkt)
@info "initialize_variables!"
@btime MadNLP.initialize_variables!(
    MadNLP.full(solver.x),
    MadNLP.full(solver.xl),
    MadNLP.full(solver.xu),
    solver.opt.bound_push, solver.opt.bound_fac,
)
println()


println("KKT solver")
@info "jtprod!"
@btime MadNLP.jtprod!(solver.jacl, solver.kkt, solver.y)
@info "set_aug_diagonal!"
@btime MadNLP.set_aug_diagonal!(solver.kkt, solver)
@info "set_aug_rhs!"
@btime MadNLP.set_aug_rhs!(solver, solver.kkt, solver.c)
@info "finish_aug_solve!"
# @btime MadNLP.finish_aug_solve!(solver.kkt, solver.d)
@btime MadNLP.finish_aug_solve!(solver, solver.kkt, solver.mu)
println()

println("Linear solver")
@info "factorize_wrapper!"
@btime MadNLP.factorize_wrapper!(solver)
@info "solve_refine_wrapper!"
@btime MadNLP.solve_refine_wrapper!(solver, solver.d, solver.p)
println()

println("Termination criteria")
@info "get_inf_pr"
@btime MadNLP.get_inf_pr(solver.c)
@info "get_inf_du"
@btime MadNLP.get_inf_du(
    MadNLP.full(solver.f),
    MadNLP.full(solver.zl),
    MadNLP.full(solver.zu),
    solver.jacl,
    1.0,
)
@info "get_inf_compl"
@btime MadNLP.get_inf_compl(
    solver.x_lr,
    solver.xl_r,
    solver.zl_r,
    solver.xu_r,
    solver.x_ur,
    solver.zu_r,
    0.0,
    1.0,
)
println()

println("Line-search")
@info "get_theta"
theta = @btime MadNLP.get_theta(solver.c)
@info "get_varphi"
@btime MadNLP.get_varphi(solver.obj_val, solver.x_lr, solver.xl_r, solver.xu_r, solver.x_ur, solver.mu)
@info "get_varphi_d"
varphi_d = @btime MadNLP.get_varphi_d(
    MadNLP.primal(solver.f),
    MadNLP.primal(solver.x),
    MadNLP.primal(solver.xl),
    MadNLP.primal(solver.xu),
    MadNLP.primal(solver.d),
    solver.mu,
)
@info "get_alpha_max"
@btime MadNLP.get_alpha_max(
    MadNLP.primal(solver.x),
    MadNLP.primal(solver.xl),
    MadNLP.primal(solver.xu),
    MadNLP.primal(solver.d),
    solver.tau,
)
@info "get_alpha_z"
@btime MadNLP.get_alpha_z(
    solver.zl_r,
    solver.zu_r,
    MadNLP.dual_lb(solver.d),
    MadNLP.dual_ub(solver.d),
    solver.tau,
)
@info "get_alpha_min"
@btime MadNLP.get_alpha_min(
    theta,
    varphi_d,
    solver.theta_min,
    solver.opt.gamma_theta,
    solver.opt.gamma_phi,
    solver.opt.alpha_min_frac,
    solver.opt.delta,
    solver.opt.s_theta,
    solver.opt.s_phi,
)
println()

println("Misc.")
@info "adjust_boundary!"
@btime MadNLP.adjust_boundary!(solver.x_lr,solver.xl_r,solver.x_ur,solver.xu_r,solver.mu)
@info "reset_bound_dual! #1"
@btime MadNLP.reset_bound_dual!(
    MadNLP.primal(solver.zl),
    MadNLP.primal(solver.x),
    MadNLP.primal(solver.xl),
    solver.mu,solver.opt.kappa_sigma,
)
@info "reset_bound_dual! #2"
@btime MadNLP.reset_bound_dual!(
    MadNLP.primal(solver.zu),
    MadNLP.primal(solver.xu),
    MadNLP.primal(solver.x),
    solver.mu,solver.opt.kappa_sigma,
)

finalize(nlp)