)]}'
{
  "commit": "a257e2aa4eb47ad340915136228cd28d918e8dc2",
  "tree": "43405a5bb8fce56208252bd0bf3e80b22eb0d9cb",
  "parents": [
    "7e1f6b6b22fb226c47b6c73caa2ebfc44bf881a5"
  ],
  "author": {
    "name": "Ming Yan",
    "email": "ming.yan@terapines.com",
    "time": "Wed May 06 17:00:48 2026 +0800"
  },
  "committer": {
    "name": "GitHub",
    "email": "noreply@github.com",
    "time": "Wed May 06 17:00:48 2026 +0800"
  },
  "message": "[SCEV] Introduce loop-uniform SCEV classification. (#194304)\n\nThis patch extends `ScalarEvolution::LoopDisposition` with a new\n`LoopUniform` state to describe SCEVs that are invariant across all\niterations of a given loop, but may still depend on inner-loop induction\nvariables.\n\nUnlike `LoopInvariant`, which requires the value to be fully invariant\nwith respect to the loop, LoopUniform captures expressions that do not\ndepend on the loop’s own induction variables, yet may vary in nested\nloops. This distinction is useful for analyses and optimizations that\nreason about per-iteration stability at a specific loop level.\n\nExample:\n```\nfor (i)\n  for (j)\n    dep(j);       // uniform w.r.t. i\n    dep(i, j);    // not uniform w.r.t. i\n```\n\nThis enables us to more precisely reason about the behavior of SCEV in\nthe context of nested loops, which is very useful for optimizations such\nas `LoopUnrollAndJam` and `OuterLoopVectorization`.",
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