hey Josh, that makes sense to me. your benchmark, though, I may not be understanding; it looks as if Julia is slower for larger matrices. is this true, or am I just going crazy and not able to properly read graphs anymore?
On Thursday, January 8, 2015 at 12:46:29 PM UTC-6, Joshua Adelman wrote: > > numpy.dot is calling BLAS under the hood so it's calling fast code and I > wouldn't expect Julia to shine against it. Try calling numpy methods that > aren't thin wrappers around C and you should see a bigger difference. Or > implement a larger more complex algorithm. Here's a simple micro-benchmark > I did recently of Julia vs np.vander: > > http://nbviewer.ipython.org/gist/synapticarbors/26910166ab775c04c47b > > Not large, but maybe a bit more illustrative. > > Josh > > > On Jan 8, 2015, at 1:27 PM, Dakota St. Laurent wrote: > > 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 :) > > >
