Gerenciamento t?rmico e energ?tico em MPSoCs

• Ano de defesa: 2017
• Autor(a) principal: Castilhos, Guilherme Machado de
• Orientador(a): Moraes, Fernando Gehm
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Pontif?cia Universidade Cat?lica do Rio Grande do Sul
• Programa de Pós-Graduação: Programa de P?s-Gradua??o em Ci?ncia da Computa??o
• Departamento: Escola Polit?cnica
• País: Brasil
• Palavras-chave em Português: MPSoC; Ger?ncia T?rmica; Ger?ncia Energ?tica; Confiabilidade
• Palavras-chave em Inglês: Thermal Management; Energy Management; Reliability
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/8336

Thermal cycles and high temperatures can have a significant impact on the systems performance, power consumption and reliability, which is a major and increasingly critical design metric in emerging multi-processor embedded systems. Existing thermal management techniques rely on physical sensors to provide them temperature values to regulate the system?s operating temperature and thermal variation at runtime. However, on-chip thermal sensors present limitations (e.g., extra power and area cost), which may restrict their use in large-scale systems. In this context, this Thesis proposes a lightweight software-based runtime temperature model, enabling to capture detailed temperature distribution information of multiprocessor systems with negligible overhead in the execution time. The temperature model is embedded in a distributedmemory MPSoC platform, described at the RTL level. Results show that the average absolute temperature error estimation, compared to the HotSpot tool is smaller than 4% in systems with up to 36 processing elements. Task mapping is the process selected to act in the system, using the temperature information generated by the proposed model. Task mapping is the process of assigning a processing element to execute a given task. The number of cores in many-core systems increases the complexity of the task mapping. The main concerns of task mapping for large systems include (i) scalability; (ii) dynamic workload; and (iii) reliability. It is necessary to distribute the mapping decisions across the system to ensure scalability. The workload of emerging many-core systems may be dynamic, i.e., new applications may start at any moment, leading to different mapping scenarios. Therefore, it is necessary to execute the mapping process at runtime to support dynamic workload. The workload assignment plays a major role in the many-core system reliability. Load imbalance may generate hotspots zones and consequently thermal implications. Recently, task mapping techniques aiming at improving system reliability have been proposed in the literature. However, such approaches rely on centralized mapping decisions, which are not scalable. To address these challenges, the main goal of this Thesis is to propose a hierarchical runtime mapping heuristic, which provides scalability and fair thermal distribution. Thermal distribution inside the system increases the system reliability in long-term, due to the reduction of hotspot regions. The proposed mapping heuristic considers the PE temperature as a cost function. The proposal adopts a hierarchical thermal monitoring scheme, able to estimate at runtime the instantaneous temperature at each processing element. The mapping uses the temperature estimated by the monitoring scheme to guide the mapping decision. Results compare the proposal against a mapping heuristic whose main cost function minimizes the communication energy. Results obtained in large systems, show a decrease in the maximum temperature (best case, 8%) and improvement in the thermal distribution (best case, 50% lower standard deviation of processor temperatures). Such results demonstrate the effectiveness of the proposal. Also, a 45% increase in the lifetime of the system was achieved in the best case, using the proposed mapping.