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Old 2017-02-02, 23:12   #16
VBCurtis
 
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"Curtis"
Feb 2005
Riverside, CA

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Quote:
Originally Posted by GeoffreyY View Post
One more question, is the polynomial searching non-deterministic? I think I got different results passing through the same range twice.

Also, are there any particular aspect / coefficient of the polynomial I should be looking for, besides the Murphy E-score? Or are they simply data points that don't carry much weight?
1. msieve searches a subset of the search space for each leading coefficient, randomizing which piece each run to minimize repeated work if multiple people run the same range. It was my impression that my tight stage1-norm reduced the search space so much that msieve didn't do this, but your results suggest otherwise. So, sort-of deterministic? :)

2. Score is an estimate of poly effectiveness; it is imperfect but within 3-5% of accurate; that is, two polys of the same score can differ by up to 5% speed in use. For jobs this size, folks usually test any poly that scores within 3-4% of the best poly (say, within 0.15 in your case) in case the best one happens to run poorly and 2nd or 3rd place runs well. If you find nothing better than 3.30, I wouldn't bother with test sieving; just use the 3.4699 poly I posted.

Best wishes on your effort- post if you have further questions, such as what parameters to select. The choices are made around line 520 in factmsieve.py, and formulas are easily edited to nudge the script to pick a different siever or large-prime bound. Hopefully, it picks 15e rather than 14e (part of the name of the lasieve executable), and chooses 2^31 as large prime bound (in a .job file created by the running script, you'll see lbpr and lpba; those should be 31 for a job this size, with 32 acceptable but 30 probly bad).

Last fiddled with by VBCurtis on 2017-02-02 at 23:17
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