Empirical Installation of Linear Algebra Shared-Memory Subroutines for Auto-Tuning
The introduction of auto-tuning techniques in linear algebra shared-memory routines is analyzed. Information obtained in the installation of the routines is used at running time to take some decisions to reduce the total execution time. The study is carried out with routines at different levels (mat...
| Autores: | , , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2014 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositório: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglês |
| OAI Identifier: | oai:riunet.upv.es:10251/49284 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/49284 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Linear algebra libraries Linear algebra routines Empirical installation Shared-memory Auto-tuning CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL |
| Resumo: | The introduction of auto-tuning techniques in linear algebra shared-memory routines is analyzed. Information obtained in the installation of the routines is used at running time to take some decisions to reduce the total execution time. The study is carried out with routines at different levels (matrix multiplication, LU and Cholesky factorizations and linear systems symmetric or general routines) and with calls to routines in the LAPACK and PLASMA libraries with multithread implementations. Medium NUMA and large cc-NUMA systems are used in the experiments. This variety of routines, libraries and systems allows us to obtain general conclusions about the methodology to use for linear algebra shared-memory routines auto-tuning. Satisfactory execution times are obtained with the proposed methodology. |
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