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A compiler processes the code written in a high level language and producesmachine executable code. The compiler writers often face the challenge ofkeeping the compilation times reasonable. That is because aggressiveoptimization passes which potentially will give rise to high performance areoften expensive in terms of running time and memory footprint. Consequently thecompiler designers arrive at a compromise where they either simplify theoptimization algorithm which may decrease the performance of the produced code,or they will restrict the optimization to the subset of the overall inputprogram in which case large parts of the input application will goun-optimized.  The problem we address in this paper is that of keeping the compilation timesreasonable, and at the same time optimizing the input program to the fullestextent possible. Consequently, the performance of the produced code will matchthe performance when all the aggressive optimization passes are applied overthe entire input program.

Categorization of Program Regions for Agile Compilation using Machine Learning and Hardware Support