Are you timing these in the global scope? That will cause a substantial performance loss. — John
On Feb 21, 2014, at 6:18 PM, Michael Schnall-Levin <[email protected]> wrote: > I've been doing some benchmarking of Julia vs Scipy for sparse matrix > multiplication and I'm finding that julia is significantly (~4X - 5X) faster > in some instances. > > I'm wondering if I'm doing something wrong, or if this is really true. Below > are some code snippets for Julia and python. Any help would be very > appreciated! > > ----- Julia: > Elapsed Time on my laptop: 24.9 seconds ----- > x_inds = Int[] > y_inds = Int[] > vals = Int[] > > for n = 1:10000 > inds = rand(1:2000,10,1) > for ind in inds > push!(x_inds, ind) > push!(y_inds, n) > push!(vals,1) > end > end > > x = sparse(x_inds, y_inds, vals, 2000, 10000) > > t = time() > for j = 1:250 > y = x*transpose(x) > end > print(string(time() - t, "\n")) > ----- > > ---- Python Elapsed Time on my laptop: 5.8 seconds ----- > import numpy > import scipy.sparse > import time > > x_inds = [] > y_inds = [] > vals = [] > for n in xrange(10000): > inds = numpy.random.randint(0, 2000,10) > > for ind in inds: > x_inds.append(ind) > y_inds.append(n) > vals.append(1) > > x_inds = numpy.array(x_inds) > y_inds = numpy.array(y_inds) > vals = numpy.array(vals) > > x = scipy.sparse.csc_matrix((vals, (x_inds, y_inds)), shape=(2000, 10000)) > > > t = time.time() > for j in xrange(250): > y = x*x.transpose() > print time.time() - t >
