forked from OSchip/llvm-project
96ab8726a3
Summary: Implements PR889 Removing the virtual table pointer from Value saves 1% of RSS when doing LTO of llc on Linux. The impact on time was positive, but too noisy to conclusively say that performance improved. Here is a link to the spreadsheet with the original data: https://docs.google.com/spreadsheets/d/1F4FHir0qYnV0MEp2sYYp_BuvnJgWlWPhWOwZ6LbW7W4/edit?usp=sharing This change makes it invalid to directly delete a Value, User, or Instruction pointer. Instead, such code can be rewritten to a null check and a call Value::deleteValue(). Value objects tend to have their lifetimes managed through iplist, so for the most part, this isn't a big deal. However, there are some places where LLVM deletes values, and those places had to be migrated to deleteValue. I have also created llvm::unique_value, which has a custom deleter, so it can be used in place of std::unique_ptr<Value>. I had to add the "DerivedUser" Deleter escape hatch for MemorySSA, which derives from User outside of lib/IR. Code in IR cannot include MemorySSA headers or call the MemoryAccess object destructors without introducing a circular dependency, so we need some level of indirection. Unfortunately, no class derived from User may have any virtual methods, because adding a virtual method would break User::getHungOffOperands(), which assumes that it can find the use list immediately prior to the User object. I've added a static_assert to the appropriate OperandTraits templates to help people avoid this trap. Reviewers: chandlerc, mehdi_amini, pete, dberlin, george.burgess.iv Reviewed By: chandlerc Subscribers: krytarowski, eraman, george.burgess.iv, mzolotukhin, Prazek, nlewycky, hans, inglorion, pcc, tejohnson, dberlin, llvm-commits Differential Revision: https://reviews.llvm.org/D31261 llvm-svn: 303362 |
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README
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.