masterThesis
Granularity load scheduling auto-tuning for multi-core processors applied to reverse-time migration
Fecha
2020-01-14Registro en:
FERNANDES, João Batista. Granularity load scheduling auto-tuning for multi-core processors applied to reverse-time migration. 2020. 75f. Dissertação (Mestrado em Engenharia Elétrica e de Computação) - Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2020.
Autor
Fernandes, João Batista
Resumen
Reverse-time migration (RTM) is an algorithm widely used in the oil and gas industry
to process seismic data. It is a computationally intensive task that can be designed to run
in parallel computers. Because of it being massive and regular, this type of task is often
equally and statically distributed among the available parallel processors. However, this
strategy might often not be optimal. When the processors are heterogeneous, and even
when most have similar processing power, many of them might still have to wait idly
for the slower processors. In this paper, we show that even among homogeneous cores
here might be load imbalance that can considerably affect the overall performance of a 3D
RTM application. We show that dynamic load distribution has a significant advantage over
the conventional static distribution, and other default OpenMP schedules, such as auto and
guided. However, the granularity of the dynamically distributed chunks of work plays a
key role in harvesting this advantage. In order to find the optimal granularity, we propose a
coupled simulated annealing (CSA) based auto-tuning strategy that adjusts the chunk size
of the work that OpenMP parallel loops assign dynamically to worker threads during the
initialization of a 3D RTM application. Experiments performed on computational systems
with different processor and memory specifications for different sizes of input show that
the proposed method is consistently faster than the default OpenMP loop schedulers.