@PHDTHESIS{ 2018:238895582,
 	title = {Multi-objective resource management for many-core systems},
 	year = {2018},
 	url = "http://tede2.pucrs.br/tede2/handle/tede/8096",
 	abstract = "Many-core systems integrate several cores in a single die to provide high-performance computing in multiple market segments. The newest technology nodes introduce restricted power caps so that results in the utilization-wall (also known as dark silicon), i.e., the on-chip power dissipation prevents the use of all resources at full performance simultaneously. The workload of many-core systems includes real-time (RT) applications, which bring the application throughput as another constraint to meet. Also, dynamic workloads generate valleys and peaks of resources utilization over the time. This scenario, complex high-performance systems subject to power and performance constraints, creates the need for multi-objective resource management (RM) able to dynamically adapt the system goals while respecting the constraints. Concerning RT applications, related works apply a design-time analysis of the expected workload to ensure throughput constraints. To cover this limitation, design-time decisions, this Thesis proposes a hierarchical Runtime Energy Management (REM) for RT applications as the first work to link the execution of RT applications and RM under a power cap without design-time analysis of the application set. REM employs different mapping and DVFS (Dynamic Voltage Frequency Scaling) heuristics for RT and non-RT tasks to save energy. Besides not considering RT applications, related works do not consider the workload variation and propose single-objective RMs. To tackle this second limitation, single-objective RMs, this Thesis presents a hierarchical adaptive multi-objective resource management (MORM) for many-core systems under a power cap. MORM addresses dynamic workloads with peaks and valleys of resources utilization. MORM can dynamically shift the goals to prioritize energy or performance according to the workload behavior. Both RMs (REM and MORM), are multi-objective approaches. This Thesis employs the Observe-Decide-Act (ODA) paradigm as the design methodology to implement REM and MORM. The Observing consists on characterizing the cores and on integrating hardware monitors to provide accurate and fast power-related information for an efficient RM. The Actuation configures the system actuators at runtime to enable the RMs to follow the multi-objective decisions. The Decision corresponds to REM and MORM, which share the Observing and Actuation infrastructure. REM and MORM stand out from related works regarding scalability, comprehensiveness, and accurate power and energy estimation. Concerning REM, evaluations on many-core systems up to 144 cores show energy savings from 15% to 28% while keeping timing violations below 2.5%. Regarding MORM, results show it can drive applications to dynamically follow distinct objectives. Compared to a stateof- the-art RM targeting performance, MORM speeds up the workload valley by 11.56% and the workload peak by up to 49%.",
 	publisher = {Pontif?cia Universidade Cat?lica do Rio Grande do Sul},
 	scholl = {Programa de P?s-Gradua??o em Ci?ncia da Computa??o},
 	note = {Escola Polit?cnica}
}