Analytical Communication Performance Models as a metric in the partitioning of data-parallel kernels on heterogeneous platforms
Data partitioning on heterogeneous HPC platforms is formulated as an optimization problem. The algorithm departs from the communication performance models of the processes representing their speeds and outputs a data tiling that minimizes the communication cost. Traditionally, communication volume i...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad Nacional de Educación a Distancia |
| Repositorio: | e-spacio. Repositorio Institucional de la UNED |
| Idioma: | inglés |
| OAI Identifier: | oai:e-spacio.uned.es:20.500.14468/24376 |
| Acceso en línea: | https://hdl.handle.net/20.500.14468/24376 |
| Access Level: | acceso abierto |
| Palabra clave: | 12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática partitioning algorithms communication performance models communication optimization hybrid data-parallel kernels |
| Sumario: | Data partitioning on heterogeneous HPC platforms is formulated as an optimization problem. The algorithm departs from the communication performance models of the processes representing their speeds and outputs a data tiling that minimizes the communication cost. Traditionally, communication volume is the metric used to guide the partitioning, but such metric is unable to capture the complexities introduced by uneven communication channels and the variety of patterns in the kernel communications. We discuss Analytical Communication Performance Models as a new metric in partitioning algorithms. They have not been considered in the past because of two reasons: prediction inaccuracy and lack of tools to automatically build and solve kernel communication formal expressions. We show how communication performance models fit the specific kernel and platform, and we present results that equal or even improve previous volume-based strategies. |
|---|