Hi,
I tried the @parallel decorator with Singular's reduce routine:
basically, I passed a bunch of polynomials in a list. The result took
*much* longer, unlike (say) the examples with factor().
sage: R.<x,y> = GF(32003)
sage: p = 0
sage: for i in range(100):
....: p = p + x^(2*i)*y^i
sage: @parallel()
def reduceme(p, B):
return p.reduce(B)
....:
sage: %time _ = list( reduceme([(p, F) for each in range(30)]))
CPU times: user 0.22 s, sys: 0.05 s, total: 0.26 s
Wall time: 0.33 s
sage: %time _ = [ reduceme(p, F) for each in range(30)]
CPU times: user 0.10 s, sys: 0.00 s, total: 0.10 s
Wall time: 0.10 s
The machine I tried this on has 4 cores, so it isn't for a lack of
processors. I've had similar results on other machines.
Is there a technical reason for this (Singular related), or am I doing
something wrong (Sage and/or John Perry related)?
regards
john perry
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