Szczegóły publikacji
Opis bibliograficzny
High performance evolutionary computation with tensor-based acceleration / Jonatan Kłosko, Mateusz Benecki, Grzegorz Wcisło, Jacek DAJDA, Wojciech TUREK // W: GECCO'22 [Dokument elektroniczny] : proceedings of the 2022 Genetic and Evolutionary Computation Conference : July 9–13, 2022, Boston, Massachusetts, [vol. 2]. — Wersja do Windows. — Dane tekstowe. — New York : The Association for Computing Machinery, cop. 2022. — e-ISBN: 978-1-4503-9327-2. — S. 805–813. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 813, Abstr. — Publikacja dostępna online od: 2022-07-08
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 141561 |
|---|---|
| Data dodania do BaDAP | 2022-09-28 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3512290.3528753 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | Genetic and Evolutionary Computations 2022 |
Abstract
Optimization methods, including evolutionary algorithms, need increasing efficiency in order to solve more and more complex problems in the shortest possible time. Apart from tuning the structure of those algorithms and adapting them to the given problem, the best way to speed up computations is introducing more parallelism, either at the hardware level (by using accelerators), or at the architecture level (by using multiple compute nodes). In this paper, we propose a method for expressing evolutionary computations, based on the tensor computational model. The presented approach enables cross-platform hardware acceleration on CPUs, GPUs and TPUs. To validate the new method, we contribute an open, extensible evolutionary framework, with support for distributed, accelerated execution in heterogeneous environments. Finally, we demonstrate results of the conducted tests that confirm the efficiency of the proposed approach, also in comparison to other existing frameworks.
