hi all, I've been trying to test some simple benchmarks for my new job to
see what language we should use between Python (Numpy/Scipy) and Julia. I
like how simple it seems for Julia to do things in parallel (we plan to be
running code on a supercomputer using lots and lots of cores), but I'm not
getting the ideal benchmarks. I'm sure I'm doing something wrong here.
Python code:
import time, numpy as np
N = 25000
A = np.random.rand(N,N)
x = np.random.rand(N)
t0 = time.clock()
A.dot(x)
print time.clock() - t0
--------------------------------
Julia code:
function rand_mat_vec_mul(A::Array{Float64, 2}, x::Array{Float64,1})
tic()
A * x
toc()
end
# warmup
rand_mat_vec_mul(rand(1000,1000), rand(1000))
rand_mat_vec_mul(rand(1000,1000), rand(1000))
rand_mat_vec_mul(rand(1000,1000), rand(1000))
# timing
rand_mat_vec_mul(rand(25000,25000), rand(25000))
---------------------------
Python generally takes about 0.630 - 0.635 seconds, Julia generally takes
about 0.640 - 0.650 seconds. as I said, I'm sure I'm doing something wrong,
I'm just not really sure what. any help is appreciated :)
