Publicação
Scheduling scientific workloads on an heterogeneous server
| Resumo: | The goal of this dissertation is to explore techniques to improve the efficiency and performance level of scientific applications on computing platforms that are equipped with multiple multi-core devices and at least one many-core device, such as Intel MIC and/or NVidia GPU devices. These platforms are known as heterogeneous servers, which are becoming increasingly popular both in research environments as in our daily gadgets. To fully exploit the performance capabilities of the heterogeneous servers, it is crucial to have an efficient workload distribution among the available devices; however the heterogeneity of the server and the workload irregularity dramatically increases the challenge. Most state of the art schedulers efficiently balance regular workloads among heterogeneous devices, although some lack adequate mechanisms for irregular workloads. Scheduling these type of workloads is particularly complex due to their unpredictability, namely on their execution time. To overcome this issue, this dissertation presents an efficient dynamic adaptive scheduler that efficiently balances irregular workloads among multiple devices in a heterogeneous environment. To validate the scheduling mechanism, the case study used in this thesis is an irregular scientific application that has a set of independent embarrassingly parallel tasks applied to a very large number of input datasets, whose tasks durations have an unpredictable range larger than 1:100. By dynamically adapting the size of the workloads that were distributed among the multiple devices in run-time, the scheduler featured in this dissertation had an occupancy rate of every computing resources over 97% of the application’s run-time while generating an overhead well below 0.001%. |
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| Autores principais: | Maia, John Camilo Ferreira |
| Assunto: | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
| Ano: | 2016 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | The goal of this dissertation is to explore techniques to improve the efficiency and performance level of scientific applications on computing platforms that are equipped with multiple multi-core devices and at least one many-core device, such as Intel MIC and/or NVidia GPU devices. These platforms are known as heterogeneous servers, which are becoming increasingly popular both in research environments as in our daily gadgets. To fully exploit the performance capabilities of the heterogeneous servers, it is crucial to have an efficient workload distribution among the available devices; however the heterogeneity of the server and the workload irregularity dramatically increases the challenge. Most state of the art schedulers efficiently balance regular workloads among heterogeneous devices, although some lack adequate mechanisms for irregular workloads. Scheduling these type of workloads is particularly complex due to their unpredictability, namely on their execution time. To overcome this issue, this dissertation presents an efficient dynamic adaptive scheduler that efficiently balances irregular workloads among multiple devices in a heterogeneous environment. To validate the scheduling mechanism, the case study used in this thesis is an irregular scientific application that has a set of independent embarrassingly parallel tasks applied to a very large number of input datasets, whose tasks durations have an unpredictable range larger than 1:100. By dynamically adapting the size of the workloads that were distributed among the multiple devices in run-time, the scheduler featured in this dissertation had an occupancy rate of every computing resources over 97% of the application’s run-time while generating an overhead well below 0.001%. |
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