首页|Researchers at Tianjin University Report New Data on Machine Learning (Machine L earning-driven Gcc Loop Unrolling Optimization: Compiler Performance Enhancement Strategy Based On Xgboost)
Researchers at Tianjin University Report New Data on Machine Learning (Machine L earning-driven Gcc Loop Unrolling Optimization: Compiler Performance Enhancement Strategy Based On Xgboost)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating in Tianjin, People’s Rep ublic of China, by NewsRx journalists, research stated, “In contemporary compile rs, the determination of the loop unrolling factor is traditionally based on man ually crafted heuristic rules. This approach heavily relies on human intuition, which limits its ability to achieve optimized performance across diverse archite ctures and can sometimes even lead to performance declines.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Tianjin. The news reporters obtained a quote from the research from Tianjin University, “ Additionally, developers face challenges in achieving cross-platform compatibili ty, often necessitating extensive redesign efforts. In response, this study intr oduces a method leveraging the XGBoost algorithm to predict the optimal loop unr olling factor for compiler optimization, thereby aiming to replace human thinkin g with machine learning methods and standardize development processes. Initially , the study gathers data on the loop unrolling factors as determined by profile guided optimization technology, analyzes program-specific loop feature vectors a nd employs cross-validation, including the Pearson correlation coefficient and f eature importance ranking, to construct a dataset. Subsequent use of XGBoost to train this dataset models the decision-making process for selecting the most eff ective loop unrolling factor. The final step involves integrating XGBoost’s trai ned decision tree model into GCC to calculate the optimal loop unrolling factor during actual compilation. Empirical results on the RISC-V platform indicate tha t this new method, when tested against the SPEC CPU 2006 benchmark suite, offers up to 6.18% improvement in performance over the existing heuristi c approach.”
TianjinPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningTianjin University
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