A dynamic multi¿Objective approach for dynamic load balancing in heterogeneous systems
Fecha
2020Resumen
Modern standards in High Performance Computing (HPC) have started to consider energy consumption and power draw as
a limiting factor. New and more complex architectures have been introduced in HPC systems to afford these new restrictions, and
include coprocessors such as GPGPUs for intensive computational tasks. As systems increase in heterogeneity, workload distribution
becomes a more core problem to achieve the maximum efficiency in every computational component. We present a Multi-Objective
Dynamic Load Balancing (DLB) approach where several objectives can be applied to tune an application. These objectives can be
dynamically exchanged during the execution of an algorithm to better adapt to the resources available in a system. We have
implemented the Multi–Objective DLB together with a generic heuristic engine, designed to perform multiple strategies for DLB in
iterative problems. We also present Ull Multiobjective Framework (UllMF), an open–source tool that implements the Multi-Objective
generic approach. UllMF separates metric gathering, objective functions to be optimized and load balancing algorithms, and improves
code portability using a simple interface to reduce the costs of new implementations. We illustrate how performance and energy
consumption are improved for the implemented techniques, and analyze their quality using different DLB techniques from the literature.