rex <[EMAIL PROTECTED]> [2007-04-16 15:53]: > I'm about to build numpy using Intel's MKL 9.1 beta and want to compare > it with the version I built using MKL 8.1. Is the LINPACK > benchmark the most appropriate?
I'm buried in responses. Not. A well-known benchmark (Scimark?) coded using NumPy/SciPy might help people realize that they don't have to use a compiled language for their problem. Alas, I can't find much in the way of benchmarks coded using NumPy/SciPy. All I've found is LINPACK, coded using Numarray. import numarray, time import numarray.random_array as naRA import numarray.linear_algebra as naLA n = 1000 a = naRA.random([n, n]) b = naRA.random([n, 1]) t = -time.time() x = naLA.solve_linear_equations(a, b) t += time.time() r = numarray.dot(a, x) - b r_n = numarray.maximum.reduce(abs(r)) print t, 2.0e-9 / 3.0 * n**3 / t print r_n, r_n / (n * 1e-16) Scimark is a broader test, but AFAIK it's only available in Java and C. FWIW, one of my PCs was the first to break a gigaflop using Scimark. Its score is 1043, which is 44% higher than the 2nd place score. http://math.nist.gov/cgi-bin/ScimarkSummary -rex -- Neutrinos have bad breadth. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
