452Highlights • Derivation of the Craig-Bampton, MacNeal, Rubin and dual Craig-Bampton method in a common framework. • Highlighting the differences and emphasizing the similarities between the four methods. • Assessing the accuracy of the methods. • Evaluation of the sparsity pattern of the reduced matrices. • Discussion of the negative eigenvalues inherent to the dual Craig-Bampton method.
Profiling feedback is an important technique used by developers for performance debugging, where it is usually used to pinpoint performance bottlenecks and also to find optimization opportunities. Assessing the validity and potential benefit of a program transformation requires accurate knowledge of the data flow and dependencies, which can be uncovered by profiling a particular execution of the program.In this work we develop poly-prof, an end-to-end infrastructure for dynamic binary analysis, which produces feedback about the potential to apply complex program rescheduling. Our tool can handle both inter-and intraprocedural aspects of the program in a unified way, thus providing interprocedural transformation feedback.
To optimize code effectively, compilers must deal with memory dependencies. However, the state-of-the-art heuristics available in the literature to track memory dependencies are inherently imprecise and computationally expensive. Consequently, the most advanced code transformations that compilers have today are ineffective when applied on real-world programs. The goal of this paper is to solve this conundrum through dynamic disambiguation of pointers. We provide different ways to determine at runtime when two memory locations can overlap. We then produce two versions of a code region: one that is aliasing-free - hence, easy to optimize - and another that is not. Our checks let us safely branch to the optimizable region. We have applied these ideas on Polly-LLVM, a loop optimizer built on top of the LLVM compilation infrastructure. Our experiments indicate that our method is precise, effective and useful: we can disambiguate every pair of pointer in the loop intensive Polybench benchmark suite. The result of this precision is code quality: the binaries we generate are 10% faster than those that Polly-LLVM produces without our optimization, at the -O3 optimization level of LLVM.
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