[Numpy-discussion] Openmp support (was numpy's future (1.1 and beyond): which direction(s) ?)

[James Philbin]

OK, i've written a simple benchmark which implements an elementwise
multiply (A=B*C) in three different ways (standard C, intrinsics, hand
coded assembly). On the face of things the results seem to indicate
that the vectorization works best on medium sized inputs. If people
could post the results of running the benchmark on their machines
(takes ~1min) along with the output of gcc --version and their chip
model, that wd be v useful.

It should be compiled with: gcc -msse -O2 vec_bench.c -o vec_bench

Here's two:

CPU: Core Duo T2500 @ 2GHz
gcc --version: gcc (GCC) 4.1.2 (Ubuntu 4.1.2-0ubuntu4)
        Problem size              Simple              Intrin              Inline
                 100   0.0003ms (100.0%)   0.0002ms ( 67.7%)   0.0002ms ( 50.6%)
                1000   0.0030ms (100.0%)   0.0021ms ( 69.2%)   0.0015ms ( 50.6%)
               10000   0.0370ms (100.0%)   0.0267ms ( 72.0%)   0.0279ms ( 75.4%)
              100000   0.2258ms (100.0%)   0.1469ms ( 65.0%)   0.1273ms ( 56.4%)
             1000000   4.5690ms (100.0%)   4.4616ms ( 97.6%)   4.4185ms ( 96.7%)
            10000000  47.0022ms (100.0%)  45.4100ms ( 96.6%)  44.4437ms ( 94.6%)

CPU: Intel Xeon E5345 @ 2.33Ghz
gcc --version: gcc (GCC) 4.1.2 20070925 (Red Hat 4.1.2-33)
        Problem size              Simple              Intrin              Inline
                 100   0.0001ms (100.0%)   0.0001ms ( 69.2%)   0.0001ms ( 77.4%)
                1000   0.0010ms (100.0%)   0.0008ms ( 78.1%)   0.0009ms ( 86.6%)
               10000   0.0108ms (100.0%)   0.0088ms ( 81.2%)   0.0086ms ( 79.6%)
              100000   0.1131ms (100.0%)   0.0897ms ( 79.3%)   0.0872ms ( 77.1%)
             1000000   5.2103ms (100.0%)   3.9153ms ( 75.1%)   3.8328ms ( 73.6%)
            10000000  54.1815ms (100.0%)  51.8286ms ( 95.7%)  51.4366ms ( 94.9%)

James
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