20220428, 21:17  #56 
"Curtis"
Feb 2005
Riverside, CA
15AF_{16} Posts 
Not really; you'll have a slightly higher duplicate rate, meaning you may need an extra 13M raw relations to get the matrix size you expect. Sieve time will barely change.

20220429, 17:37  #57 
"Ed Hall"
Dec 2009
Adirondack Mtns
11744_{8} Posts 
Here's the c165:
Code:
N = 309... <165 digits> 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 Polynomial Selection (size optimized): Total time: 523377 Polynomial Selection (root optimized): Total time: 35527 Lattice Sieving: Total time: 4.96142e+06s (all clients used 4 threads) Lattice Sieving: Total number of relations: 175009615 Found 122644730 unique, 53361072 duplicate, and 0 bad relations. cownoise Best MurphyE for polynomial is 7.58493957e13 
20220429, 20:50  #58 
"Curtis"
Feb 2005
Riverside, CA
12657_{8} Posts 
If I've got the scaling of poly score vs sieve time, looks like 88 is quicker.

20220429, 21:08  #59  
"Ed Hall"
Dec 2009
Adirondack Mtns
11744_{8} Posts 
Quote:
ETA: I'm planning to try the Colab session, but the compression and upload times, as well as Colab inialization, are going to take quite a while, so I'm not sure there's an advantage. ETA2: After nearly an hour and nowhere near completion, I gave up on my msieve.dat.mat.tbz upload to Google Drive for a Colab test. It had already taken over a halfhour to copy and compress it. Last fiddled with by EdH on 20220429 at 22:25 Reason: why do I keep writing startegey?. . . 

20220527, 20:04  #60 
"Ed Hall"
Dec 2009
Adirondack Mtns
1001111100100_{2} Posts 
Here's a c162 I just finished:
Code:
N = 314... <162 digits> tasks.I = 14 tasks.lim0 = 45000000 tasks.lim1 = 70000000 tasks.lpb0 = 31 tasks.lpb1 = 32 tasks.qmin = 17000000 tasks.sieve.lambda0 = 1.84 tasks.sieve.mfb0 = 59 tasks.sieve.mfb1 = 62 tasks.sieve.ncurves0 = 20 tasks.sieve.ncurves1 = 25 tasks.sieve.qrange = 10000 Polynomial Selection (size optimized): Total time: 494305 Polynomial Selection (root optimized): Total time: 28582 Lattice Sieving: Total time: 3.96443e+06s (all clients used 4 threads) Lattice Sieving: Total number of relations: 209104683 Found 146771345 unique, 45989579 duplicate, and 0 bad relations. cownoise Best MurphyE for polynomial is 1.11487621e12 
20220527, 20:23  #61 
"Curtis"
Feb 2005
Riverside, CA
12657_{8} Posts 
Looks like 78% slower than the scaling one would expect for a C162 given the timing data from the C165 (that is, using the "double effort every 5.5 digits" heuristic).
I'd expect 3.72 megaseconds based on the 4.96 megasecond time for C165, calculated by inverting the ratio of the poly scores; the run was 3.96 megaseconds, 8% slower than target. This may indicate 3 large primes is valuable on the C160 file, or perhaps some other parameter is not ideal. I'd try the lims and mfbs from the C165 run, with 31/31LP and 5% lower target relations (because the job is smaller/easier). I'd also use the qmin from the C165. Ignoring the changes made for 3LP, I've been scaling the lim's to double every 10 digits. I'm not sure what is best for 3LP cases, but C165 used 60M/40M; perhaps 50M for lim0 makes sense on C160 file. 
20220527, 21:46  #62 
"Ed Hall"
Dec 2009
Adirondack Mtns
2^{2}×19×67 Posts 
Thanks! I've possibly got a c160 (615...) coming up. I think I've got the changes made to the params.* I'll post what turns up.
* I've got a high rels_wanted, but Msieve is going to break in when a matrix is possible. 
20220528, 16:28  #63 
"Ed Hall"
Dec 2009
Adirondack Mtns
5092_{10} Posts 
Here's the latest:
Code:
N = 615... <160 digits> tasks.I = 14 tasks.lim0 = 50000000 tasks.lim1 = 40000000 tasks.lpb0 = 31 tasks.lpb1 = 31 tasks.qmin = 7000000 tasks.sieve.lambda0 = 1.83 tasks.sieve.mfb0 = 58 tasks.sieve.mfb1 = 89 tasks.sieve.ncurves0 = 20 tasks.sieve.ncurves1 = 25 tasks.sieve.qrange = 5000 Polynomial Selection (size optimized): Total time: 493434 Polynomial Selection (root optimized): Total time: 27103.8 Lattice Sieving: Total time: 3.40375e+06s (all clients used 4 threads) Lattice Sieving: Total number of relations: 169962886 Found 119992365 unique, 35401629 duplicate, and 0 bad relations. cownoise Best MurphyE for polynomial is 1.64176010e12 
20220528, 18:19  #64 
"Curtis"
Feb 2005
Riverside, CA
1010110101111_{2} Posts 
Hrmmm.... poly score nearly 50% better than the last one, but only 15% less sieve time. That's bad especially when raw relations needed went from 209M to 169M.
Aha! I forgot: Breaking 3LP cofactors uses a small ncurves value. Leaving ncurves1 at 25 may have caused this slow job. I think the CADO default is to use ncurves of 9 or 10 on the 3LP side (2025 on the 2LP side is still good). So, when using mfb1 > 2 * lpb1 which is how we do 3LP on that side, drop ncurves1 from 25 to 10ish. 
20220528, 21:02  #65 
"Ed Hall"
Dec 2009
Adirondack Mtns
13E4_{16} Posts 
I'm running a sequence that is currently at 169 digits on its way down. It has a good chance of providing some more 16x composites. I'll drop ncurves1 to 10 for the next one, which could actually be any of the three param files (160, 165, 170). I'm somewhat set for 160 and 165, but what would you suggest for 170 if I start with the 165 params?

20220614, 13:38  #66 
"Ed Hall"
Dec 2009
Adirondack Mtns
2^{2}×19×67 Posts 
Another c163
Code:
N = 344... <163 digits> tasks.I = 14 tasks.lim0 = 60000000 tasks.lim1 = 40000000 tasks.lpb0 = 31 tasks.lpb1 = 31 tasks.qmin = 7000000 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 Polynomial Selection (size optimized): Total time: 529204 Polynomial Selection (root optimized): Total time: 30833.4 Lattice Sieving: Total time: 4.95571e+06s (all clients used 4 threads) Lattice Sieving: Total number of relations: 185882690 Found 125042962 unique, 43355067 duplicate, and 0 bad relations. cownoise Best MurphyE for polynomial is 9.45211518e13 
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