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Old 2021-08-31, 16:51   #3092
bur
 
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If it makes sense I could also test-sieve, though only with 10 cores. If so, which would be a good set of parameters?
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Old 2021-08-31, 18:52   #3093
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There's nothing stopping you from test-sieving, but it wouldn't be useful for me because I'm comparing speeds as well as yields for lots of different parameter choices, and for speeds to be consistent I have to run everything on the same machine.
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Old 2021-08-31, 20:56   #3094
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Ok, then I won't get into it.
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Old 2021-08-31, 23:33   #3095
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Test-sieving done, at least for today.

I only used I=16, lpb 34/35, and given the yields I don't expect anything smaller than this to be better. Even if 35/35 does sieve faster, it will make postprocessing more of a pain. The machine I used only has 16GB RAM so I can't test A=32 which requires ~26GB, but I don't think we're at a size where that would be clearly better.

Raising ncurves0 from 25 doesn't find any more relations. It could possibly be dropped a bit, but 2LP cofactorization is a tiny contribution to the total time so the effect would be negligible. ncurves1=21 does miss a few relations, but it's just a fraction of a percent, so with the extra 3LP cofactorization time I don't think there's any need to change this from the default.

There is a clear benefit of several percent from using -adjust-strategy 2, as seems to be standard with large jobs. The price paid for this is that memory use with I=16 is 13GB rather than 10GB, but I assume this won't be a problem.

Higher lims = more yield and slower sieving, and with the added complication of the duplication rate depending on the lim choice, it's not easy to measure the effects with test-sieving. 500M/800M probably isn't optimal but it'll be close enough.

mfb0 choice is always complicated by the fact that higher mfb0 leads to a small increase in the number of relations required, and we don't have a good handle on this. This makes 68 less attractive, but in the interests of keeping yield high I stuck with 67 for the main test.

mfb1 can go quite high and still show a decent improvement in sec/rel. There doesn't seem to be any problem with going as high as 102.

I test-sieved the 500M/800M, 67/102 combination at various intervals between 200M and 2500M. Sec/rel worsened from ~0.7 to ~1.5 over this range. If the aim is 3G raw relations, the range 250M-2000M looks about right, and I estimate this will take only(!) ~105 thread-years on the machine I used. This figure seems suspiciously low even given that the threads in question are fast, but I can't find a mistake anywhere, so I have to believe it. Hopefully this doesn't mean Ryan's in for a nasty surprise with the duplication rate!

Last fiddled with by charybdis on 2021-08-31 at 23:37
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Old 2021-09-01, 02:45   #3096
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I appreciate the detail in the writeup- for e.g. Bur who sees a future in test-sieving, that post is a nice place to start.
Here's the conclusion for Ryan:
Code:
tasks.lim0 = 500000000
tasks.lim1 = 800000000
tasks.lpb0 = 34
tasks.lpb1 = 35
tasks.sieve.mfb0 = 67
tasks.sieve.mfb1 = 102
tasks.sieve.ncurves0 = 25
tasks.sieve.ncurves1 = 21
tasks.I = 16
tasks.qmin = 250000000
tasks.sieve.qrange = 5000
tasks.sieve.adjust_strategy = 2
tasks.sieve.rels_wanted = 3000000000
If memory is plentiful, consider running A=32 for the first ~500M relations for the extra yield at small Q, and then changing down to I=16. That should help with the duplicate-relations rate, too- making it more likely to get 2G unique relations before Q=2000M.
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Old 2021-09-01, 03:02   #3097
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I'd make one small change for practical purposes, which is to set rels_wanted artificially high (say 4G rather than 3G) as the intention is presumably to run msieve filtering. We don't want CADO filtering to be triggered, for a couple of reasons:

1. There is a bug whereby sieving with adjust_strategy = 2 can produce a relation with a (square) composite factor. This is extremely rare, but in 3G relations there's a decent chance it will happen, and unlike msieve, I don't think CADO filtering handles it well.
2. Even if this doesn't happen, CADO filtering on a job this big likely requires hundreds of GB of memory.

Setting a large value of rels_wanted has the added benefit that ETA values are more accurate early in the sieving while the yield is high
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Old 2021-09-01, 17:19   #3098
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Quote:
for e.g. Bur who sees a future in test-sieving, that post is a nice place to start.
That sound like test-sieving is a career-option. Professional test-siever.



But seriously, thanks, I always enjoy these posts.
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Old 2021-09-12, 18:56   #3099
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Quote:
Originally Posted by VBCurtis View Post
I appreciate the detail in the writeup- for e.g. Bur who sees a future in test-sieving, that post is a nice place to start.
Here's the conclusion for Ryan:
I'm sieving now. Thanks to those who found the poly and params!

Will give an update in a few days.
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Old 2021-09-14, 13:58   #3100
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Update: I'm at 555M uniques now. I currently seem to be pulling about 250M unique (not total) relations per day, so hopefully another 6-10 days of sieving should be enough, if the rate keeps up.
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Old 2021-09-14, 16:56   #3101
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How can the percentage of uniques be found during sieving? By doing a manual filtering step on the relations?


Impressive progress anyway, about the same timeframe I need for a C167...
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Old 2021-09-14, 20:21   #3102
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Quote:
Originally Posted by bur View Post
How can the percentage of uniques be found during sieving? By doing a manual filtering step on the relations?


Impressive progress anyway, about the same timeframe I need for a C167...
remdups4 is what I use. It can be found in the ggnfs package. (ggnfs/contrib/remdups/remdups4.c)


Edit: Depending on which sieving package you are using, you will need to gather all the relations into a single file and:
Code:
[z]cat <file> | ./remdups4 1000 ><uniquerels>

Last fiddled with by EdH on 2021-09-14 at 20:24
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