Thread‐level parallelism (TLP) has been widely exploited to optimize computational resource usage in high‐performance systems. However, as many applications do not scale as the number of threads increase, resources will be wasted when the application executes with the maximum possible number of threads (i.e., the default execution) rather than fewer threads (thread throttling) that may use the resources more efficiently. Hence, instead of executing only one application with as many threads as possible, one can run more applications simultaneously by applying thread throttling to each one. The primary outcome of this strategy is a significant reduction in the total execution time and energy consumption when the system needs to execute a list of applications. Given that, we propose a smart resource allocation (SRA) for concurrent parallel application execution. It automatically finds the ideal degree of TLP for each application and guides the simultaneous parallel applications execution. When running 25 well‐known benchmarks on three multicore systems and comparing SRA to state‐of‐the‐art strategies (e.g., Batch, Equal policy, and Scalability), SRA improves the EDP by 87.4% over the Batch strategy; 75.5% over the Equal policy; and 38.8% over the scalability strategy.
A computação na nuvem emerge como uma plataforma alternativa para a execução de aplicações de alto desempenho. Simultaneamente, a atualização de nodos computacionais nestes sistemas pode levar a uma heterogeneidade de recursos. Neste sentido, o desafio de executar aplicações paralelas na nuvem não está apenas relacionado a definição do melhor número de threads para a aplicação, mas também, a escolha ideal da arquitetura que irá executar tal aplicação. No entanto, as características de grau de paralelismo e capacidade computacional têm sido pouco exploradas para fazer a alocação de aplicações numa nuvem heterogênea. Portanto, neste artigo, mostramos que ao considerar o grau de paralelismo de uma aplicação e as características do nodo computacional, ganhos significativos de desempenho e consumo de energia podem ser obtidos quando comparado a maneira padrão com que aplicações são escalonadas num ambiente de nuvem heterogênea.
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