An approach for an efficient execution of SPMD applications on Multi-core environments
Executing traditional Message Passing Interface (MPI) applications on multi-core cluster balancing speed and computational efficiency is a difficult task that parallel programmers have to deal with. For this reason, communications on multi-core clusters ought to be handled carefully in order to impr...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:160448 |
| Acceso en línea: | https://ddd.uab.cat/record/160448 https://dx.doi.org/urn:doi:10.1016/j.future.2016.06.016 |
| Access Level: | acceso abierto |
| Palabra clave: | Performance improvements Multi-core Mapping Scheduling Scalability analysis Tiling applications SPMD |
| Sumario: | Executing traditional Message Passing Interface (MPI) applications on multi-core cluster balancing speed and computational efficiency is a difficult task that parallel programmers have to deal with. For this reason, communications on multi-core clusters ought to be handled carefully in order to improve performance metrics such as efficiency, speedup, execution time and scalability. In this paper we focus our attention on SPMD (Single Program Multiple Data) applications with high communication volume and synchronicity and also following characteristics such as: static, local and regular. This work proposes a method for SPMD applications, which is focused on managing the communication heterogeneity (different cache level, RAM memory, network, etc.) on homogenous multi-core computing platform in order to improve the application efficiency. In this sense, the main objective of this work is to find analytically the ideal number of cores necessary that allows us to obtain the maximum speedup, while the computational efficiency is maintained over a defined threshold (strong scalability). This method also allows us to determine how the problem size must be increased in order to maintain an execution time constant while the number of cores are expanded (weak scalability) considering the tradeoff between speed and efficiency. This methodology has been tested with different benchmarks and applications and we achieved an average improvement around 30.35% of efficiency in applications tested using different problems sizes and multi-core clusters. In addition, results show that maximum speedup with a defined efficiency is located close to the values calculated with our analytical model with an error rate lower than 5% for the applications tested. |
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