Also, that's a 1.5% performance difference which is pretty negligible.
> On Jan 8, 2015, at 2:15 PM, Stefan Karpinski <[email protected]> > wrote: > > You're really just comparing BLASes. So the question is what BLAS is each > system using? You can find out in Julia using versioninfo(). > > >> On Jan 8, 2015, at 1:27 PM, Dakota St. Laurent <[email protected]> >> 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 :)
