On 16 Feb 2024, at 2:48 am, Marten van Kerkwijk <mhvk@astro.utoronto.ca> wrote:
In [45]: %timeit np.add.reduce(a, axis=None)
42.8 µs ± 2.44 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
In [43]: %timeit dotsum(a)
26.1 µs ± 718 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
But theoretically, sum, should be faster than dot product by a fair bit.
Isn’t parallelisation implemented for it?
I cannot reproduce that:
In [3]: %timeit np.add.reduce(a, axis=None)
19.7 µs ± 184 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
In [4]: %timeit dotsum(a)
47.2 µs ± 360 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
But almost certainly it is indeed due to optimizations, since .dot uses
BLAS which is highly optimized (at least on some platforms, clearly
better on yours than on mine!).
I thought .sum() was optimized too, but perhaps less so?
I can confirm at least it does not seem to use multithreading – with the conda-installed numpy+BLAS
I almost exactly reproduce your numbers, whereas linked against my own OpenBLAS build
In [3]: %timeit np.add.reduce(a, axis=None)
19 µs ± 111 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
# OMP_NUM_THREADS=1
In [4]: %timeit dots(a)
20.5 µs ± 164 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
# OMP_NUM_THREADS=8
In [4]: %timeit dots(a)
9.84 µs ± 1.1 µs per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
add.reduce shows no difference between the two and always remains at <= 100 % CPU usage.
dotsum is scaling still better with larger matrices, e.g. ~4 x for 1000x1000.
Cheers,
Derek
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