# [Numpy-discussion] NumPy benchmark

rex rex@nosyntax....
Tue Apr 17 02:32:53 CDT 2007

```rex <rex@nosyntax.com> [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
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