CADO-NFS Data Harvesting

[VBCurtis]

Excellent point- we want the ratio of qmin to qmax to be something between 6 and 8. I suspect your C164 with I=14 ran Q roughly 10-60M; since A=28 should yield 40% better, a Q-range of 7-43M might be expected.

So, I agree with Charybdis' suggestion to change qmin to 7M. Good idea!

[EdH]

Originally Posted by VBCurtis

Excellent point- we want the ratio of qmin to qmax to be something between 6 and 8. I suspect your C164 with I=14 ran Q roughly 10-60M; since A=28 should yield 40% better, a Q-range of 7-43M might be expected.

So, I agree with Charybdis' suggestion to change qmin to 7M. Good idea!

It has been written - it will be done.

One more c164 (231...) has shown up in my work listings and I should be able to start it tomorrow afternoon.

[EdH]

The latest is underway:

N = 231...<164 digits>
tasks.A = 28
tasks.lim0 = 60000000
tasks.lim1 = 40000000
tasks.lpb0 = 31
tasks.lpb1 = 31
tasks.qmin = 7000000
tasks.sieve.adjust_strategy = 2
tasks.sieve.lambda0 = 1.83
tasks.sieve.mfb0 = 58
tasks.sieve.mfb1 = 88
tasks.sieve.ncurves0 = 18
tasks.sieve.ncurves1 = 10
tasks.sieve.qrange = 5000
tasks.sieve.rels_wanted = 175000000

In my reports, is there any interest in the two "filter" lines? I'm thinking of removing them.

[VBCurtis]

The filter lines have no effect, since you're using msieve for postprocessing.

[EdH]

Originally Posted by VBCurtis

The filter lines have no effect, since you're using msieve for postprocessing.

Those were my thoughts, but wondered if they may have any use, anyway. Thanks!

[EdH]

The latest c164:

N = 231... <164 digits>
tasks.A = 28
tasks.lim0 = 60000000
tasks.lim1 = 40000000
tasks.lpb0 = 31
tasks.lpb1 = 31
tasks.qmin = 7000000
tasks.sieve.adjust_strategy = 2
tasks.sieve.lambda0 = 1.83
tasks.sieve.mfb0 = 58
tasks.sieve.mfb1 = 88
tasks.sieve.ncurves0 = 18
tasks.sieve.ncurves1 = 10
tasks.sieve.qrange = 5000
Polynomial Selection (size optimized): Total time: 496571
Polynomial Selection (root optimized): Total time: 31172.8
Lattice Sieving: Total time: 4.28663e+06s (all clients used 4 threads)
Lattice Sieving: Total number of relations: 169728713
Found 119819021 unique, 42731179 duplicate, and 0 bad relations.
cownoise Best MurphyE for polynomial is 8.60775398e-13

[VBCurtis]

Looks like a 6% better scoring poly than the last test, but only 4% lower sieve time.
Nearly a wash, but I=14 is also lower memory while being not-slower.

[EdH]

Originally Posted by VBCurtis

Looks like a 6% better scoring poly than the last test, but only 4% lower sieve time.
Nearly a wash, but I=14 is also lower memory while being not-slower.

Sounds like a suggestion to move back to I=14. I'm thinking I'm going to have to move up or down a digit for my next few tests. Is there a preference if I move to mfb1=89?

[VBCurtis]

As long as the input number is close, comparing E-score is pretty accurate (as I did in my previous post). It shouldn't matter whether you go with 163 or 165 or 166 next time for the mfb 89 trial.
Note that a larger mfb "should" improve yield, but more raw relations are likely necessary. The tradeoff is murky- if sec/rel is not better when test-sieving, I go with the smaller mfb. When sec/rel is better, a full factorization is likely to educate us. So, please try a full factorization as you planned.

[EdH]

All my runs are full runs, unless they break. At these sizes, full runs are less than 48 hours, so my patience is still holding.

[EdH]

I've started a c165. I went back to I=14, but forgot to take qmin back up to 10M. Will that throw things off?:

N = 309...<165>
tasks.I = 14
tasks.lim0 = 60000000
tasks.lim1 = 40000000
tasks.lpb0 = 31
tasks.lpb1 = 31
tasks.qmin = 7000000
tasks.sieve.adjust_strategy = 2
tasks.sieve.lambda0 = 1.83
tasks.sieve.mfb0 = 58
tasks.sieve.mfb1 = 89
tasks.sieve.ncurves0 = 18
tasks.sieve.ncurves1 = 10
tasks.sieve.qrange = 5000
tasks.sieve.rels_wanted = 175000000