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As discussed in https://github.com/llvm/llvm-project/issues/53020 / https://reviews.llvm.org/D116692, SCEV is forbidden from reasoning about 'backedge taken count' if the branch condition is a poison-safe logical operation, which is conservatively correct, but is severely limiting. Instead, we should have a way to express those poison blocking properties in SCEV expressions. The proposed semantics is: ``` Sequential/in-order min/max SCEV expressions are non-commutative variants of commutative min/max SCEV expressions. If none of their operands are poison, then they are functionally equivalent, otherwise, if the operand that represents the saturation point* of given expression, comes before the first poison operand, then the whole expression is not poison, but is said saturation point. ``` * saturation point - the maximal/minimal possible integer value for the given type The lowering is straight-forward: ``` compare each operand to the saturation point, perform sequential in-order logical-or (poison-safe!) ordered reduction over those checks, and if reduction returned true then return saturation point else return the naive min/max reduction over the operands ``` https://alive2.llvm.org/ce/z/Q7jxvH (2 ops) https://alive2.llvm.org/ce/z/QCRrhk (3 ops) Note that we don't need to check the last operand: https://alive2.llvm.org/ce/z/abvHQS Note that this is not commutative: https://alive2.llvm.org/ce/z/FK9e97 That allows us to handle the patterns in question. Reviewed By: nikic, reames Differential Revision: https://reviews.llvm.org/D116766 |
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Polly - Polyhedral optimizations for LLVM ----------------------------------------- http://polly.llvm.org/ Polly uses a mathematical representation, the polyhedral model, to represent and transform loops and other control flow structures. Using an abstract representation it is possible to reason about transformations in a more general way and to use highly optimized linear programming libraries to figure out the optimal loop structure. These transformations can be used to do constant propagation through arrays, remove dead loop iterations, optimize loops for cache locality, optimize arrays, apply advanced automatic parallelization, drive vectorization, or they can be used to do software pipelining.