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Two-stage compilation for optimizing and deploying programs in general purpose languages

Двухэтапная компиляция для оптимизации и развертывания программ на языках общего назначения
Authors: Arutyun Avetisyan;

Two-stage compilation for optimizing and deploying programs in general purpose languages

Abstract

We describe the approach for two-stage compilation of C/C++ programs using the LLVM compiler infrastructure that allows optimizing programs taking into account the user profile and his/her target machine features, as well as deploying programs in a cloud storage while transparently optimizing and checking for defects. The notable features of the approach are its applicability to programs written using general-purpose languages and utilizing the common compiler infrastructure on all optimization and deployment stages.On the first stage (host machine) the build process is transparently captured and the LLVM bitcode files are generated with the proper support for archive/library files. LTO optimizations are also performed at this time, and the program dependencies and installation structure (e.g. libraries, installed resource or documentation files) are also captured. On the second stage (target machine) the final code generation and installation is performed with the transparent path translation. Either static code generation or dynamic JIT-based compilation is supported.As mentioned, a cloud-based deployment strategy can also be used allowing for additional features like defect and vulnerability checking while the cloud store has access to the deployed programs in the LLVM bitcode form.While optimizing LLVM toolchain for working on ARM embedded devices we have achieved the memory consumption reduction of up to 10% and 10-20% compile time decrease.

Keywords

динамическая оптимизация, llvm, Electronic computers. Computer science, уязвимость, облачное хранилище, QA75.5-76.95

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
gold