Analyzing the performance of hierarchical collective algorithms on ARM-based multicore clusters
| Autor: | Gladys Utrera, Marisa Gil, Xavier Martorell |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Clustering algorithms
Performance ARM processors Gestió de memòria (Informàtica) Communications standard Parallel application Computer algorithms Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat [Àrees temàtiques de la UPC] Distributed memory architecture Memory management (Computer science) Multi-core cluster Shared memory Collective HPC Algorismes computacionals MPI High performance computing Standard libraries Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC] Càlcul intensiu (Informàtica) Memory architecture |
| Popis: | MPI is the de facto communication standard library for parallel applications in distributed memory architectures. Collective operations performance is critical in HPC applications as they can become the bottleneck of their executions. The advent of larger node sizes on multicore clusters has motivated the exploration of hierarchical collective algorithms aware of the process placement in the cluster and the memory hierarchy. This work analyses and compares several hierarchical collective algorithms from the literature that do not form part of the current MPI standard. We implement the algorithms on top of OpenMPI using the shared-memory facility provided by MPI-3 at the intra-node level and evaluate them on ARM-based multicore clusters. From our results, we evidence aspects of the algorithms that impact the performance and applicability of the different algorithms. Finally, we propose a model that helps us to analyze the scalability of the algorithms. This work has been supported by the Spanish Ministry of Education (PID2019-107255GB-C22) and the Generalitat de Catalunya (2017-SGR-1414). |
| Databáze: | OpenAIRE |
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