Run-time Data Analysis to Drive Compiler Optimizations

Activity: Talk or presentationContributed talkscience-to-science

Description

Throughout program execution, types may stabilize, variables may become constant, and code sections may turn out to be redundant—all information that is used by just-in-time (JIT) compilers to achieve peak performance. Yet, since JIT compilation is done on demand for individual code parts, global observations cannot be made. Moreover, global data analysis is an inherently expensive process, that collects information over large data sets. Thus, 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.
Period19 Oct 2021
Event titleSPLASH Doctoral Symposium at SPLASH'21
Event typeConference
LocationAustriaShow on map

Fields of science

  • 102029 Practical computer science
  • 102009 Computer simulation
  • 102 Computer Sciences
  • 102011 Formal languages
  • 102022 Software development
  • 102013 Human-computer interaction
  • 102024 Usability research

JKU Focus areas

  • Digital Transformation