Function inlining is a critical optimization technique used by compilers to improve program performance by replacing a function call with the body of the function and eliminating the overhead associated with function ...
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Function inlining is a critical optimization technique used by compilers to improve program performance by replacing a function call with the body of the function and eliminating the overhead associated with function calls. However, the decision of when to inline functions and when not to is a nontrivial problem due to interactions with the rest of the compiler pipeline. Incorrect inlining decisions can cause runtime performance degradation, making this problem a crucial one to study. This paper reviews the different techniques used to optimize function inlining, including simple textual substitution, profile-guided inlining, interprocedural optimization, partial inlining, speculative inlining, and advanced techniques such as indirect call optimizations. Each technique has its strengths, weaknesses, and trade-offs, and ongoing research is exploring ways to overcome these challenges. Optimizing function inlining is a complex problem, and different techniques offer different tradeoffs. Further research to improve the performance of function inlining while minimizing any potential drawbacks could be pursued based on this paper.
Predicting the execution time of computer programs is an important but challenging problem in the community of computer systems. Existing methods require experts to perform detailed analysis of program code in order t...
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ISBN:
(纸本)9781617823800
Predicting the execution time of computer programs is an important but challenging problem in the community of computer systems. Existing methods require experts to perform detailed analysis of program code in order to construct predictors or select important features. We recently developed a new system to automatically extract a large number of features from program execution on sample inputs, on which prediction models can be constructed without expert knowledge. In this paper we study the construction of predictive models for this problem. We propose the SPORE (Sparse Polynomial REgression) methodology to build accurate prediction models of program performance using feature data collected from program execution on sample inputs. Our two SPORE algorithms are able to build relationships between responses (e.g., the execution time of a computer program) and features, and select a few from hundreds of the retrieved features to construct an explicitly sparse and non-linear model to predict the response variable. The compact and explicitly polynomial form of the estimated model could reveal important insights into the computer program (e.g., features and their non-linear combinations that dominate the execution time), enabling a better understanding of the program's behavior. Our evaluation on three widely used computer programs shows that SPORE methods can give accurate prediction with relative error less than 7% by using a moderate number of training data samples. In addition, we compare SPORE algorithms to state-of-the-art sparse regression algorithms, and show that SPORE methods, motivated by real applications, outperform the other methods in terms of both interpretability and prediction accuracy.
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