| Full text | |
| Author(s): |
de Azevedo da Rocha, Ricardo Luis
;
de Freitas Barbosa, Mateus C. M.
;
Shakshuki, E
;
Yasar, A
Total Authors: 4
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF THE XI LATIN AND AMERICAN ALGORITHMS, GRAPHS AND OPTIMIZATION SYMPOSIUM; v. 130, p. 6-pg., 2018-01-01. |
| Abstract | |
This paper describes an empirical research focused on inlining, a compiler transformation that explores the idea of expanding a function's code to uncover optimization opportunities. Previous work has not addressed the problem of representing and utilizing multi-run profiles. To produce sound results on feedback-directed optimization (FDO), we employ multi-run profiles using Berube's contribution on Combined Profiling (CP). The FDO inliner (FDI) designed by Berube is already an adaptive one. We devised a new algorithm for inlining, a slight modification on Berube's produced a speedup over other inliners, including LLVM. (C) 2018 The Authors. Published by Elsevier B.V. (AU) | |
| FAPESP's process: | 17/02111-5 - Using machine learning to improve compiler decisions |
| Grantee: | Ricardo Luis de Azevedo da Rocha |
| Support Opportunities: | Regular Research Grants |