pkofod

Benchmark Report

Job Properties

Commit(s): pkofod/julia@a4daf3b21f1a98dd208f50bc1b785d4d92f24d3b vs JuliaLang/julia@83007fb8c4c748fb23d38759daafd77fa1d56c2b

Triggered By: link

Tag Predicate: ALL

pkofod

pkm@pkm:~/.julia/v0.6/RemPiO2$ julia6 -O3 test/runtests.jl 

Testing speed and accuracy of rempio2 in [-pi*9/4, pi*9/4]
----------------------------------------------------------
Numbers below are elapsed time returned from @belapsed

Every number is checked between the two implementations
using a @test such that any difference results in termi-
nation of the program.

pkofod

julia> Profile.print()
11 ./event.jl:68; (::Base.REPL.##3#4{Base.REPL.REPLBackend})()
 11 ./REPL.jl:95; macro expansion
  11 ./REPL.jl:64; eval_user_input(::Any, ::Base.REPL.REPLBackend)
   11 ./boot.jl:234; eval(::Module, ::Any)
    11 ./<missing>:?; anonymous
     11 ./profile.jl:16; macro expansion;
      11 /home/pkm/.julia/v0.5/MDPTools/src/solution/solve.jl:39; solve!;
       11 /home/pkm/.julia/v0.5/MDPTools/src/solution/solve.jl:40; #solve!#58;
        11 ./<missing>:0; (::MDPTools.#kw##solve!)(::Array{Any,1}, ::MDPTools.#solve!, ::MDPTools.LinearUtility{Float64}, ::MDP...

pkofod

intersect at /home/pkm/julia/julia/src/subtype.c:1628
intersect_ufirst at /home/pkm/julia/julia/src/subtype.c:1032 [inlined]
intersect_var at /home/pkm/julia/julia/src/subtype.c:1100
intersect at /home/pkm/julia/julia/src/subtype.c:1628
intersect_ufirst at /home/pkm/julia/julia/src/subtype.c:1032 [inlined]
intersect_var at /home/pkm/julia/julia/src/subtype.c:1100
intersect at /home/pkm/julia/julia/src/subtype.c:1628
intersect_ufirst at /home/pkm/julia/julia/src/subtype.c:1032 [inlined]
intersect_var at /home/pkm/julia/julia/src/subtype.c:1100
intersect at /home/pkm/julia/julia/src/subtype.c:1628

pkofod

using Optim, Calculus
# Let's try with a polynomial. It has very simple Hessian, as there are no cross
# products, only quadratic terms. Hence the Hessian is a diagonal matrix where
# diag(H) = [2, 2, ..., 2]. Let's try to see if that's what we get! 
function large_polynomial(x::Vector)
    res = zero(x[1])
    for i in 1:250
        res += (i - x[i])^2
    end
    return res

pkofod

using BenchmarkTools
K = 5
N = 3
z = [rand(20000, K) for i =1:N]
P = [rand(20000) for i = 1:N]
cache = zeros(20000, K)

function test1(P, z)
    for i in eachindex(P)
        cache .= P[i].*z[i]

pkofod

K = 5
N = 3
z = [rand(20000, K) for i =1:N]
P = [rand(20000) for i = 1:N]
Pm = rand(20000, N)
Pv = [@view Pm[:, j] for j = 1:N]
cache = zeros(20000, K)

function test1(P, z)
    for i in eachindex(P)

pkofod

function LearnBase.learn!(solver::CrossEntropyMethod, env::AbstractEnvironment, doanim = false)

	# !!! INIT:

	# this is a mappable function of θ to reward
	cem_episode = θ -> begin
		π = cem_policy(env, θ)
		R, T = episode!(env, π; maxiter = solver.options[:maxiter])
		R
	end

pkofod

julia> using Optim

julia> function rosenbrock(x)
               N = length(x)
               fval = 0.0
               for i in 1:div(N, 2)
                   fval += 100(x[2i-1]^2 - x[2i])^2 + (x[2i-1] - 1.0)^2
               end
               fval
           end

pkofod

using BenchmarkTools
A = rand(10000)
B = Vector[rand(10) for i = 1:1000]
C = rand(10,1000)

D = rand(10000)
E = Vector[rand(10) for i = 1:1000]
F = rand(10, 1000)

function fast!(a, c)