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#56 |
"Curtis"
Feb 2005
Riverside, CA
122378 Posts |
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Not really; you'll have a slightly higher duplicate rate, meaning you may need an extra 1-3M raw relations to get the matrix size you expect. Sieve time will barely change.
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#57 |
"Ed Hall"
Dec 2009
Adirondack Mtns
450710 Posts |
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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 7.58493957e-13 |
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#58 |
"Curtis"
Feb 2005
Riverside, CA
5,279 Posts |
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If I've got the scaling of poly score vs sieve time, looks like 88 is quicker.
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#59 | |
"Ed Hall"
Dec 2009
Adirondack Mtns
4,507 Posts |
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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 half-hour to copy and compress it. Last fiddled with by EdH on 2022-04-29 at 22:25 Reason: why do I keep writing startegey?. . . |
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