An experimental analysis of regression-obtained HPC scheduling heuristics (2023)
- Authors:
- USP affiliated authors: LEJBMAN, ALFREDO GOLDMAN VEL - IME ; ROSA, LUCAS DE SOUSA - IME
- Unidade: IME
- DOI: 10.1007/978-3-031-43943-8_6
- Subjects: HEURÍSTICA; SCHEDULING; APRENDIZADO COMPUTACIONAL; REGRESSÃO LINEAR
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: Workshop on Job Scheduling Strategies for Parallel Processing - JSSPP
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
ROSA, Lucas de Sousa e CARASTAN-SANTOS, Danilo e GOLDMAN, Alfredo. An experimental analysis of regression-obtained HPC scheduling heuristics. 2023, Anais.. Cham: Springer, 2023. Disponível em: https://doi.org/10.1007/978-3-031-43943-8_6. Acesso em: 23 fev. 2026. -
APA
Rosa, L. de S., Carastan-Santos, D., & Goldman, A. (2023). An experimental analysis of regression-obtained HPC scheduling heuristics. In Proceedings. Cham: Springer. doi:10.1007/978-3-031-43943-8_6 -
NLM
Rosa L de S, Carastan-Santos D, Goldman A. An experimental analysis of regression-obtained HPC scheduling heuristics [Internet]. Proceedings. 2023 ;[citado 2026 fev. 23 ] Available from: https://doi.org/10.1007/978-3-031-43943-8_6 -
Vancouver
Rosa L de S, Carastan-Santos D, Goldman A. An experimental analysis of regression-obtained HPC scheduling heuristics [Internet]. Proceedings. 2023 ;[citado 2026 fev. 23 ] Available from: https://doi.org/10.1007/978-3-031-43943-8_6 - Escalonamento com consciência energética para fluxos de trabalho científicos sem servidor: uma abordagem de aprendizado de máquina
- Exploring simplicity and efficiency: regression-based scheduling heuristics in HPC
- On limits of machine learning techniques in the learning ofscheduling policies
- In search of efficient scheduling heuristics from simulations and machine learning
- The influence of organizational factors on inter-team knowledge sharing effectiveness in agile environments
- Improving the performance of actor model runtime environments on multicore and manycore platforms
- Towards automatic actor pinning on multi-core architectures
- A simple BSP-based model to predict execution time in GPU applications
- A comparison of GPU execution time prediction using machine learning and analytical modeling
- Message from the program committee co-chairs. [Apresentação]
Informações sobre o DOI: 10.1007/978-3-031-43943-8_6 (Fonte: oaDOI API)
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