AbstractsEngineering

Current HPC systems have made more complex simulations feasible, yielding benefits to several research areas. To meet the increasing processing demands of these simulations, new equipment is being designed, aiming at the exaflops scale. A major challenge for building these systems is the power that they will require, which current perspectives reach the GigaWatts. To address this problem, this thesis presents an approach to increase the energy efficiency using of HPC resources, aiming to reduce the effects of load imbalance to save energy. We developed an energy-aware strategy, called ENERGYLB, which considers platform characteristics, and the load irregularity and dynamicity of the applications to improve the energy efficiency. Our strategy takes into account the current computational load and clock frequency, to decide whether to call a load balancing strategy that reduces load imbalance by migrating tasks, or use Dynamic Voltage and Frequency Scaling (DVFS) technique to adjust the clock frequencies of the cores according to their weighted loads. As different processor architectures can feature two levels of DVFS granularity, per-chip DVFS or per-core DVFS, we created two different algorithms for our strategy. The first one, FG-ENERGYLB, allows a fine control of the clock frequency of cores in systems that have few tens of cores and feature per-core DVFS control. On the other hand, CGENERGYLB is suitable for HPC platforms composed of several multicore processors that do not allow such a fine-grained control, i.e., that only perform per-chip DVFS. Both approaches exploit residual imbalances on iterative applications and combine dynamic load balancing with DVFS techniques. Thus, they reduce the clock frequency of underloaded computing cores, which experience some residual imbalance even after tasks are remapped. We evaluate the applicability of our approaches using the CHARM++ parallel programming system over benchmarks and real world applications. Experimental results present improvements in energy consumption and power demand over state-of-the-art algorithms. The energy savings with ENERGYLB used alone were up to 25%with our FG-ENERGYLB algorithm, and up to 27%with our CG-ENERGYLB algorithm. Nevertheless, residual imbalances were still present after tasks were remapped. In this case, when our approaches were employed together with these load balancers, an improvement in energy savings of up to 56% is achieved with FG-ENERGYLB and up to 36% with CG-ENERGYLB. These savings were obtained by exploiting residual imbalances on iterative applications. By combining dynamic load balancing with the DVFS technique, our approach is able to reduce the average power demand of parallel systems, reduce the task migration among the available resources, and keep load balancing overheads low. Os atuais sistemas de HPC tem realizado simulações mais complexas possíveis, produzindo benefícios para diversas áreas de pesquisa. Para atender à crescente demanda de processamento dessas simulações, novos equipamentos estão sendo… Advisors/Committee Members: Navaux, Philippe Olivier Alexandre.