摘要：The Optimization Selection Problem is widely known in computer science for its complexity and importance. Several approaches based on machine learning and iterative compilation have been proposed to mitigate this problem. Although these approaches provide several advantages, they have disadvantages that can hinder the performance. This paper proposes a hybrid approach that combines the best of machine learning and iterative compilation. Several experiments were performed using different strategies, metrics and hardware platforms. A thorough analysis of the results reveals that the hybrid approach is a considerable improvement over machine learning and iterative compilation. In addition, the hybrid approach outperforms the best compiler optimization level of LLVM. Download data is not yet available.
其他摘要：The Optimization Selection Problem is widely known in computer science for its complexity and importance. Several approaches based on machine learning and iterative compilation have been proposed to mitigate this problem. Although these approaches provide several advantages, they have disadvantages that can hinder the performance. This paper proposes a hybrid approach that combines the best of machine learning and iterative compilation. Several experiments were performed using different strategies, metrics and hardware platforms. A thorough analysis of the results reveals that the hybrid approach is a considerable improvement over machine learning and iterative compilation. In addition, the hybrid approach outperforms the best compiler optimization level of LLVM.
关键词：Compilers; optimization; optimization selection problem; iterative compilation; machine learning Abstract The Optimization Selection Problem is widely known in computer science for its complexity and importance;Several approaches based on machine learning and iterative compilation have been proposed to mitigate this problem;Although these approaches provide several advantages; they have disadvantages that can hinder the performance;This paper proposes a hybrid approach that combines the best of machine learning and iterative compilation;Several experiments were performed using different strategies; metrics and hardware platforms;A thorough analysis of the results reveals that the hybrid approach is a considerable improvement over machine learning and iterative compilation;In addition; the hybrid approach outperforms the best compiler optimization level of LLVM;Downloads Download data is not yet available.
