Dynamic scheduling in application level aware of architecture and data dependencies between tasks

In this paper, a dynamic scheduling strategy for parallel applications on NUMA architectures is presented. The aim of the strategy was reduce the impact, when executing parallel aplications, of the different latencies arising from the physical distribution of memory modules in NUMA architectures. This strategy was validated by a series of experiments, where it was possible to assess its performance by comparing it with other tools that employ scheduling on application level. This work resulted in an extension of the Anahy execution core, which now comprise the proposed strategy, considering, at the time of scheduling, the heterogeneous characteristics of the architecture where the application is running. The results show that the proposed strategy improved the quality of Anahy scheduling on NUMA architectures, contributing to the increased performance of this environment. Compared to other tools, the proposed strategy proved compatible.