target optimization: re-norm optimization scale so that 0 is best.#29926
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target optimization: re-norm optimization scale so that 0 is best.#29926
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@tgamblin this is the PR we discussed earlier |
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Ok I dug into this. I added this to packages:
ncurses:
target: [x86_64_v4]And bisected like this: git bisect start
git bisect good 8ddaa08ed2aacb4b5e587a33c625492cbdd4886e
git bisect bad develop
git bisect run bash -c '[ $(spack spec --json -lIt --reuse ncurses | jq -r ".spec.nodes[0].hash") != ljewpulsil7kybn6j7vnv7xspod6rgjy ]'You'll need to pick an installed hash and package name of your own for this to work. Note also that I reversed "bad" and "good" to get With that, it finds #29933 (268c671) as the place where this was fixed, and I can confirm that it's fixed on that commit and not fixed on Do we still need to merge this one? |
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@tgamblin I don't think we need to merge it with any urgency, but we discussed out of channel that it would be good renorm these so 0 is best. |
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This needs to be reworked to move the math back to the asp.py level, because math in clingo is killing performance. |
alalazo
previously requested changes
May 25, 2022
lib/spack/spack/solver/concretize.lp
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| not derive_target_from_parent(_, Package), | ||
| not package_target_weight(Target, Package, _). | ||
| not package_target_weight(Target, Package, _), | ||
| Offset = #count{W : package_target_weight(_, Package, W)}, |
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I agree with the PR but as discussed we should probably move the math inside asp.py, since clingo isn't particularly good at that. For instance trilinos on develop:
Time:
setup: 7.0143
load: 0.0202
ground: 2.5024
solve: 3.2076
Total: 12.9201
while with this branch:
Time:
setup: 6.9622
load: 0.0203
ground: 2.5175
solve: 15.6529
Total: 25.3843
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I think this needs to be fixed before we can merge.
tgamblin
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May 25, 2022
lib/spack/spack/solver/concretize.lp
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| not derive_target_from_parent(_, Package), | ||
| not package_target_weight(Target, Package, _). | ||
| not package_target_weight(Target, Package, _), | ||
| Offset = #count{W : package_target_weight(_, Package, W)}, |
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I think this needs to be fixed before we can merge.
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May 28, 2022
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…29926) referred targets are currently the only minimization criteria for Spack for which we allow negative values. That means Spack may be incentivized to add nodes to the DAG if they match the preferred target. This PR re-norms the minimization criteria so that preferred targets are weighted from 0, and default target weights are offset by the number of preferred targets per-package to calculate node_target_weight. Also fixes a bug in the test for preferred targets that was making the test easier to pass than it should be.
iarspider
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May 30, 2022
…pack#29926) referred targets are currently the only minimization criteria for Spack for which we allow negative values. That means Spack may be incentivized to add nodes to the DAG if they match the preferred target. This PR re-norms the minimization criteria so that preferred targets are weighted from 0, and default target weights are offset by the number of preferred targets per-package to calculate node_target_weight. Also fixes a bug in the test for preferred targets that was making the test easier to pass than it should be.
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bhatiaharsh
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Aug 8, 2022
…pack#29926) referred targets are currently the only minimization criteria for Spack for which we allow negative values. That means Spack may be incentivized to add nodes to the DAG if they match the preferred target. This PR re-norms the minimization criteria so that preferred targets are weighted from 0, and default target weights are offset by the number of preferred targets per-package to calculate node_target_weight. Also fixes a bug in the test for preferred targets that was making the test easier to pass than it should be.
vjranagit
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Jan 18, 2026
…pack#29926) referred targets are currently the only minimization criteria for Spack for which we allow negative values. That means Spack may be incentivized to add nodes to the DAG if they match the preferred target. This PR re-norms the minimization criteria so that preferred targets are weighted from 0, and default target weights are offset by the number of preferred targets per-package to calculate node_target_weight. Also fixes a bug in the test for preferred targets that was making the test easier to pass than it should be.
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Preferred targets are currently the only minimization criteria for Spack for which we allow negative values. That means Spack may be incentivized to add nodes to the DAG if they match the preferred target.
This PR re-norms the minimization criteria so that preferred targets are weighted from 0, and default target weights are offset by the number of preferred targets per-package to calculate node_target_weight.
Also fixes a bug in the test for preferred targets that was making the test easier to pass than it should be.