Compiler and Runtime System Optimizations for the Insieme Compiler Infrastructure

Developing programs that fully utilize the available computing capabilities of the underlying hardware has always been a challenge. The introduction of high-level programming languages and their optimizing compilers aimed at shielding developers from platform specific details and foster the creation of efficient applications. Following the adoption of parallelism in hardware along with the associated complexity and error potential, specialized task-parallel runtime systems intended to ease the creation of correct and maintainable parallel programs were developed. Still, most research in this area is focused on just one of these two components of the toolchain -- either the compiler or the runtime system. By broadening our view and researching on combined compiler and runtime system optimizations, we can tap into further optimization potential. This goal is achieved by forwarding static analysis results obtained during the compile-time of a program to the runtime system. Along with dynamic data gathered by the runtime system itself, this information enables improved scheduling decisions and more precise parameter settings within a runtime system. This thesis describes research in three different areas of combined compiler and runtime system optimizations. First, we investigate the cache- and performance impacts of code multi-versioning for parallel programs. Second, we study and develop runtime system parameter optimization by means of compiler analyses. Third, we research compiler analyses for progress estimation of parallel programs. We base our work on the Insieme compiler and runtime system, which offers a source-to-source compiler with a parallelism-aware intermediate representation fostering the development of high-level analyses and transformations. In combination with Insieme's parallel runtime system, this enables us to research novel ideas and exploit additional optimization potential for parallel programs along the whole toolchain.Developing programs that fully utilize the available computing capabilities of the underlying hardware has always been a challenge. The introduction of high-level programming languages and their optimizing compilers aimed at shielding developers from platform specific details and foster the creation of efficient applications. Following the adoption of parallelism in hardware along with the associated complexity and error potential, specialized task-parallel runtime systems intended to ease the creation of correct and maintainable parallel programs were developed. Still, most research in this area is focused on just one of these two components of the toolchain -- either the compiler or the runtime system. By broadening our view and researching on combined compiler and runtime system optimizations, we can tap into further optimization potential. This goal is achieved by forwarding static analysis results obtained during the compile-time of a program to the runtime system. Along with dynamic data gathered by the runtime system itself, this information enables improved scheduling decisions and more precise parameter settings within a runtime system. This thesis describes research in three different areas of combined compiler and runtime system optimizations. First, we investigate the cache- and performance impacts of code multi-versioning for parallel programs. Second, we study and develop runtime system parameter optimization by means of compiler analyses. Third, we research compiler analyses for progress estimation of parallel programs. We base our work on the Insieme compiler and runtime system, which offers a source-to-source compiler with a parallelism-aware intermediate representation fostering the development of high-level analyses andAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersArbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprüftInnsbruck, Univ., Diss., 2019(VLID)443073