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PAARes: an efficient process allocation based on the available resources of cluster nodes

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Abstract

The process allocation is a problem in high performance computing, especially when using heterogeneous architectures involving diverse performance characteristics such as number of cores and their frequencies, multithreading technologies, cache memory etc. In order to improve the application performance, it is necessary to consider which processing units are the most suitable to execute the application processes. In this paper, PAARes (Process Allocation based on the Available Resources) strategy is implemented that automatically collects the system information for the process distribution by considering the processing capacity of each node in a cluster and their available resources. To demonstrate the efficiency and efficacy of the proposed strategy, the NAS (NASA Advanced Supercomputing) parallel benchmark is run on homogeneous and heterogeneous clusters under both dedicated and non-dedicated environments.

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Notes

  1. In OpenMPI a slot is an allocation unit for a process.

  2. Communications where the sender, receiver and message are well defined

  3. Process that generates load on the CPU performing floating point operations.

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Authors and Affiliations

  1. Universidad Autónoma Metropolitana, Mexico City, Mexico

    J. L. Quiroz-Fabián, G. Román-Alonso, M. A. Castro-García & M. Aguilar-Cornejo

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  1. J. L. Quiroz-Fabián
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  2. G. Román-Alonso
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  3. M. A. Castro-García
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  4. M. Aguilar-Cornejo
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Contributions

Dr. José Luis and Dr. Graciela wrote the main manuscript text, Dr. Miguel and Dr. Manuel did the experiments, Dr. José Luis and Dr. Manuel prepared figures, and Dr. Graciela and Dr. Miguel prepared tables. All authors conceived of the presented idea, discussed the results and contributed to the final manuscript.

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Correspondence to J. L. Quiroz-Fabián.

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Quiroz-Fabián, J.L., Román-Alonso, G., Castro-García, M.A. et al. PAARes: an efficient process allocation based on the available resources of cluster nodes. J Supercomput 79, 10423–10441 (2023). https://doi.org/10.1007/s11227-023-05085-7

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