Abstract
The widespread use of heterogeneous computing platforms, as well as the incorporation of computationally expensive implementations of intelligent data analysis algorithms into modern software systems leads to the demand in moving software fragments to most suitable hardware accelerators that are available on a heterogeneous computing platform. In this research, we propose an approach to the generation of recommendations for improving the performance of software systems by finding candidate algorithm implementations for hardware acceleration, and by suggesting the most suitable hardware accelerator among the specialized processors that are available on a given heterogeneous computing platform. The proposed approach is based on a code-to-code search technique, which extracts code fragments from an abstract syntax tree (AST), converts them into vectors containing program features, and compares the vectors with the query program vector. The obtained results confirm that the use of automatically recommended hardware accelerators for the code fragments identified using the proposed approach indeed allows to increase the performance of software systems solving machine learning tasks.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Open Information Technologies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
