Run-time Data Analysis to Drive Compiler Optimizations
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ACM Student Research Competition at SPLASH'21
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Dynamic compilers collect a variety of information to optimize programs and achieve peak performance. Nevertheless, particularly in data-heavy applications, analysis of the processed data - data structures, metrics, relations - could enable additional optimizations in terms of access patterns and data locality. Query planning in database systems is one source of inspiration, but due to the required overhead to collect such information, it is infeasible in dynamic compilers. With this project, we propose integrating data analysis into a dynamic runtime to speed up big data applications. The goal is to use the detailed run-time information for speculative compiler optimizations based on the shape and complexion of the data to improve performance.