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EnglishModern heterogeneous platforms require compilers capable of choosing the appropriate device for the execution of program portions. This paper presents a machine learning method designed for supporting mapping decisions through the analysis of the program source code represented in LLVM assembly language (IR) for exploiting the advantages offered by this generalised and optimised representation. To evaluate our solution, we trained an LSTM neural network on OpenCL kernels compiled in LLVM-IR and processed with our tokenizer capable of filtering less-informative tokens. We tested the network that reaches an accuracy of 85% in distinguishing the best computational unit.
Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR / Barchi, Francesco; Urgese, Gianvito; Macii, Enrico; Acquaviva, Andrea. - ELETTRONICO. - (2019). ((Intervento presentato al convegno In 2019 56th ACM/ESDA/IEEE Design Automation Conference (DAC) tenutosi a Las Vegas USA nel 2-6 June 2019 [10.1145/3316781.3317789].
| Titolo: | Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR | |
| Autori: | ||
| Data di pubblicazione: | 2019 | |
| Abstract: | Modern heterogeneous platforms require compilers capable of choosing the appropriate device for t...he execution of program portions. This paper presents a machine learning method designed for supporting mapping decisions through the analysis of the program source code represented in LLVM assembly language (IR) for exploiting the advantages offered by this generalised and optimised representation. To evaluate our solution, we trained an LSTM neural network on OpenCL kernels compiled in LLVM-IR and processed with our tokenizer capable of filtering less-informative tokens. We tested the network that reaches an accuracy of 85% in distinguishing the best computational unit. | |
| ISBN: | 978-1-4503-6725-7 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 08806950_post_print_editor.pdf | Articolo principale | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
| document_post_print.pdf | Articolo principale | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2726074
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